The intention of this study was to differentiate liquid gasoline samples from casework as well as different brands of liquid gasoline purchased from local gas stations by utilizing multivariate pattern recognition methods on data from gas chromatography-mass spectrometry. A supervised learning approach was undertaken to achieve this goal employing the methods of principal component analysis, canonical variate analysis, orthogonal canonical variate analysis, linear discriminant analysis, support vector machines and neural networks. The study revealed that the variability in the sample of gasolines form casework was sufficient enough to distinguish all the samples from one another using the classical methods of discrimination. New results using support vector machines and neural networks will be presented.