Chemometric processing of differential scanning calorimetry (DSC) curves is rarely approached in the literature. This paper discusses and presents an example of building discriminant models against the presence of a compound in pharmaceutical formulations. The dataset containing 11 curves of pure substances and 21 signals of various OTC tablets was processed by partial least squares discriminant analysis (PLS-DA) against detection of these substances in the formulations. Good model was found in the case of acetaminophen (5 PLS factors included), due to good proportion between positive and negative training samples. The model was validated on external prediction set and was found to have following parameters: RMS equal to 0.1068 (98.8% of explained variance), RMSECV equal to 0.148 (97.7% of explained variance) and external predictive error (RMSEP) equal to 0.3918 (86.5% of explained variance). (C) 2010 Elsevier B.V. All rights reserved.