Soft independent modeling of class analogy (SIMCA) K-nearest neighbor method (KNN) Support vector machines (SVM) Probabilistic neural network (PNN) Near infrared (NIR) spectroscopy Near-infrared (NIR) spectroscopy is a non-destructive measurement technique for many chemical compounds that has proved its efficiency for laboratory and industrial applications (including petroleum industry). Motor oil classification is an important task for quality control and identification of oil adulteration. Type of motor oil base stock is a key factor in product price formation. In this paper we have tried to evaluate the efficiency of different methods for motor oils classification by base stock (synthetic, semi-synthetic and mineral) and kinematic viscosity at low and high temperature. We have compared the abilities of seven ( 7) different classification methods: regularized discriminant analysis (RDA), soft independent modelling of class analogy (SIMCA), partial least squares classification (PLS), K-nearest neighbour (KNN), artificial neural networkmultilayer perceptron (ANN-MLP), support vector machine (SVM), and probabilistic neural network (PNN)for classification of motor oils. Three (3) sets of near-infrared spectra (1125, 1010, and 1050 items) were used for classification of motor oils into three or four classes. In all cases NIR spectroscopy was found to be effective for motor oil classification when combined with an effective multivariate data analysis (MDA) technique. SVM and PNN chemometric techniques were found to be the most effective ones for classification of motor oil based on its NIR spectrum.