Our results show that, in general, the IR data is more consistent for predicting functional groups than MS data, a conclusion backed by chemical intuition. However, several functional group predictions benefit from the inclusion of MS data. Additionally, our model architecture is more optimal for analysis of IR data due to the continuous nature of these spectra, and the mathematical structure of an MLP model. Our model's performance is not affected by the number of functional groups present in the training data and it predicted all the functional groups consistently across all metrics. Moreover, several known chemical patterns in the spectra were identified as features for the model to identify common functional groups without any expert training of the system. We conclude that a multi-class, multi-label perspective is apt for further studies which may combine differing spectroscopic data types that may reveal unknown features useful for the identification of compounds. We show that our approach for functional group predictions is flexible as it can be extended to introduce new or sub-divide existing functional groups without affecting performance of original functional group definitions. Furthermore, reducing chemical spectral data in a latent space does affect model performance to predict functional groups but can be used for inverse design of molecules based on combination of spectral properties. Finally, we have verified that our model also produces reasonable results for a mixture of compounds containing multiple, different, functional groups. Overview of the MLP methodology for the classification of functional groups using FTIR and MS data. FTIR spectra are processed as to normalize the transmittance of the spectra and discretize the wavenumber numbers (creating wavenumber bins), thereby standardizing the wavenumbers for all FTIR spectra. Missing wavenumber bins in each spectrum are interpolated using B-Splines. A similar process is used for mass spectra data with the exception that no interpolation is performed. The normalized transmittance in all bins is encoded into a latent space by an autoencoder network and This latent space this then used to predict the functional group of a molecule.