Freeze dried coffee, filter coffee, tea and cola were analysed by Curie-point pyrolysis mass spectrometry (PyMS). Cluster analysis showed, perhaps not surprisingly, that the discrimination between coffee, tea and cola was very easy. However, cluster analysis also indicated that there was a secondary difference between these beverages which could be attributed to whether they were caffeine-containing or decaffeinated. Artificial neural networks (ANNs) could be trained, with the pyrolysis mass spectra from some of the freeze dried coffees, to classify correctly the caffeine status of the unseen spectra of freeze dried coffee, filter coffee, tea and cola in an independent test set. However, the information in terms of which masses in the mass spectrum are important was not available, which is why ANNs are often perceived as a ‘black box’ approach to modelling spectra. By contrast, genetic programs (GPs) could also be used to classify correctly the caffeine status of the beverages, but which evolved function trees (or mathematical rules) enabling the deconvolution of the spectra and which highlighted that m/z 67, 109 and 165 were the most significant masses for this classification. Moreover, the chemical structure of these mass ions could be assigned to the reproducible pyrolytic degradation products from caffeine.