In this work, we develop a simple-to-implement and reproducible method to identify the presence of given functional groups in IR spectra based on machine learning. We employ neural networks to automate the learning of features from the data, enabling the identification of functional groups present in given molecules. Our method overcomes the disadvantages of previous work, such as being able to predict multiple functional groups from a single model, avoiding the use of images and mass spectra data in order to get higher scores. Our goal is to develop a fully automated data-driven method that can be easily integrated into electronic lab notebooks for chemistry such as Chemotion . As a starting point, we use the problem definition of Fine et al. , with some changes referring to the use of the SMARTS, as the functional groups used for the study are within our scope. A side goal of our work is the establishment and publication of an open-access IR spectra database. This database will be used here to test our method, but we also intend it to serve for benchmarking efforts in future studies by other researchers, in order to allow the quantitative comparison of different methods on a clearly defined, well-documented, and openly accessible dataset. This open-access reference dataset can be used in addition to the well-known NIST datasets -allowing the generation of a seed database to be extended in the long run.