Raman spectroscopy was used to classify a group of seized counterfeit medications associated with erectile dysfunction. Using appropriate data preprocessing, principal component analysis and the classification method soft independent modelling of class analogy (SIMCA), it was possible to classify genuine from unregistered generic and counterfeit Cialis® batches. However, SIMCA did not effectively classify samples on the basis of their active pharmaceutical ingredient (API). Partial least squares discriminant analysis, principal component regression and support vector machines effectively distinguished between the API of the samples but were unable to correctly distinguish all samples as genuine or generic/counterfeit. This study highlights the importance of choosing the correct preprocessing procedures and classification method for the data set. Despite diverse tablet contents in addition to the drug, it was possible to quantify the levels of drug in the medicines as high, medium or low (within ±20mgg tablet concentration). Overall, the potential for Raman spectroscopy combined with multivariate analysis for qualitative and semiquantitative analysis of counterfeit medicines was demonstrated, and the approach may be used to determine the potential level of harm in counterfeit medicines on the basis of API identity and amount.