Near infrared spectroscopy (NIRS) is a non-destructive measurement technique with broad application in pharmaceutical industry. Correct identification of pharmaceutical ingredients is an important task for quality control. Failure in this step can result in several adverse consequences, varied from economic loss to negative impact on patient safety. We have compared different methods in classification of a set of commercially available structurally related contrast media, Iodixanol (Visipaque(®)), Iohexol (Omnipaque(®)), Caldiamide Sodium and Gadodiamide (Omniscan(®)), by using NIR spectroscopy. The performance of classification models developed by soft independent modelling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA) and Main and Interactions of Individual Principal Components Regression (MIPCR) were compared. Different variable selection methods were applied to optimize the classification models. Models developed by backward variable elimination partial least squares regression (BVE-PLS) and MIPCR were found to be most effective for classification of the set of contrast media. Below 1.5% of samples from the independent test set were not recognized by the BVE-PLS and MIPCR models, compared to up to 15% when models developed by other techniques were applied.