The purpose of the present paper is to study the classification methods of gasoline. First, two classific models are compared using discriminant cluster analysis method in 700-1 100 nm and 1 100-1 700 nm spectral region. The sample is 90#, 93# and 97# gasoline. The results show that the model in 1 100-1 700 nm spectral region is veracious. And then a new model has been educed based on principal component analysis (PCA) and self-organizing competitive neural networks in order to classify 90#, 93# and 97# gasoline. The spectral data were condensed by PCA method before modeling, and three principal components were chosen because their cumulative credibility had reached 97%. A three-layer self-organizing competitive neural network model was established. Thirty-two wavelengths’ absorbance is the concentrated spectral data by PCA method, and served as inputs of the self-organizing competitive neural network. The learning parameter is set as 0.01 and the training iteration is taken as 500. The conclusion is that it is feasible to apply near infrared spectroscopy to discriminate the gasoline products as the PCA and self-organizing competitive neural networks method is used. Also the PCA and self-organizing competitive neural networks method is better than the discriminant cluster analysis method.