Spectral features from specific regions in infrared spectra of organic molecules can consistently be attributed to certain functional groups. Artificial neural networks were employed as a pattern recognition tool to elucidate the relationships between functional groups and spectral features. The ability of these network models to predict the presence and absence of a variety of functional groups was evaluated. The sensitivity of the artificial neural network over the entire infrared spectral region was used to generate a spectral factor representation of the major information associated with each functional group. The resulting sensitivity factors were utilized in a much simpler model for functional group prediction. Ultimately, the presence of a functional group was predicted based on the dot product of an unknown spectrum with the corresponding sensitivity factor. A probability based on Bayes' theorem was assigned to each of the predictions. The prediction accuracies were greater than 90% for all 13 functional groups considered in the investigation.