Several analytical techniques aimed at profiling drugs are deemed costly and time consuming, and may not be promptly available for analysis when required. This paper proposes a method for identifying the analytical techniques providing the most relevant data for classification of drug samples into authentic and unauthentic categories. For that matter, we integrate principal components analysis (PCA) to k-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classification tools. PCA is first applied to data from five techniques, i.e., physical profile, X-ray fluorescence (XRF), direct infusion electrospray ionization mass spectrometry (ESI-MS), active pharmacological ingredients profile (ultra performance liquid chromatography, UPLC–MS), and infrared spectroscopic profile (ATR-FTIR). Subsets of PCA scores are then combined with a “leave one subset out at a time” approach, and the classification accuracy using KNN and SVM evaluated after each subset is omitted. Subsets yielding the maximum accuracy indicate the techniques to be prioritized in profiling applications. When applied to data from Viagra and Cialis, the proposed method recommended using the data from UPLC–MS, physical profile and ATR-FTIR techniques, which increased the categorization accuracy. In addition, the SVM classification tool is suggested as more accurate when compared to the KNN.