Machine learning applied to chemistry is a growing field of research. Many factors, such as advancements in graphics processing units, larger dataset collections and new algorithms have contributed to this renaissance. Baum et al. observe that this growth is not uniform and postulate the reason fields such as analytical chemistry have seen faster developments compared to others like organic synthesis is due to the availability of large training datasets in areas which are traditionally more data intensive. For example, in analytical chemistry, machine learning algorithms have been used to find chemical species at concentrations below the usual limit of detection by finding hidden patterns of signals within the noise. However, considerable progress has been made in applying machine learning to organic chemistry. For example, machine learning techniques have been used effectively in computer aided synthesis planning by training on reaction data from Reaxys or the United States Patent and Trademark Office dataset. Sensor data such as in-situ temperature and pH can be a good source for machine learning algorithms in time series modelling. Interest in sensor usage has grown with the development of the internet of things (IoT) -which is the exchange of data between internet-connected devices, and can be extended to include chemistry equipment and chemically relevant data. The concepts of cloud chemistry or telechemistry have been introduced and involve remotely monitoring reactions by uploading the results from analytical equipment to the cloud. IoT is part of the wider concept of industry 4.0, which has received attention in recent years and refers to the current trends of interconnectivity, data, and automation. The application of machine learning techniques to real-time data, including sensor data, for predictive maintenance is an example of industry 4.0 practice. Within process chemistry, these principles have been applied to enable predictive maintenance for pilot plants and selfoptimisation of reactions. The use of sensors and other in-line methods for process monitoring was part of the vision for process analytical techniques set out by the United States Food and Drug Administration in 2004. These methods are typically non-destructive and real-time, offering advantages over traditional sampling methods. The use of sensors in organic chemistry is an emerging area, fuelled by advances in flow and automated synthesis. Using sensors inside organic chemistry reactions generates data which could be valuable in the development of machine learning tools to augment the synthesis process, ultimately helping the chemist. In the field of reaction kinetics, hybrid models, which combine machine learning with traditional modelling methods, have found success at predicting the chemo-and regioselectivity of substitution reactions. Sensors in chemistry can be used to monitor the reaction or to control the processes involved in performing the reaction. Mettler Toledo's ReactIR™ is used for monitoring reactions in real time using infrared (IR) spectroscopy, while FlowIR™ is an adaptation of this designed for use in flow chemistry where additional sensors measure pressure and temperature to monitor the flow within the system. In automation, conductivity sensors have been used to detect the phase boundary between two immiscible solvents during extraction. There has been work to standardise the hardware and code used to run experiments to improve reproducibility of results and data sharing. Automation has been used effectively to explore unknown chemical space and predict the reactivity using the NMR spectra from before and after the reaction which has led to the discovery of novel transformations. Despite significant advances in automation, most synthetic chemistry in the lab is done manually in glassware as batch chemistry. In this study, we explore the utility of using sensor data gathered from hand-performed reactions. We use data collected by DeepMatter's DigitalGlassware platform and a mix of proprietary and original equipment manufacturer sensor devices. Specifically, these consist of a DeviceX™ reaction probe (temperature, ultraviolet (UV), pressure, stir rate and camera) and a Vernier thermometer, both suitable for a multi-necked flask, and an environmental sensor which would go adjacent to the reaction setup. The sensors used in this study recorded and more importantly saved the data in an open extensible markup language format. The proposed utility focuses on predicting current and future product formation to track the reaction progress. NMR and chromatography are two methods commonly used for monitoring reaction progress. To be quantitative, UV chromatography requires calibration of the UV detector with the analyte. This can be a challenge because authentic samples of the analyte may not be available. Alternative detection methods such as evaporative light scattering detection and chemiluminescent nitrogen detection are less accurate, but do not need calibration with the sample. NMR provides quantitative results with use of an internal standard but can be more challenging to interpret and may require more extensive sample preparation and interpretation. In hand-performed reactions, these methods take time for the chemist to perform and valuable machine time. Sample preparation means these methods are limited to being applied during chemists' worktime, whereas reactions often run overnight or over non-workdays.