Gas chromatography（GC） was used to determinate the composition and contents of fatty acids in vegetable oils,including olive oil,peanut oil,rapeseed oil and soybean oil.And the GC fingerprint profile was employed for the fingerprint analysis and species classification of the four species of vegetable oils.11 feature variables were selected by successive projections algorithm（SPA）.Then,principal component analysis（PCA） and three supervised pattern recognition models:radial basis function artificial neural natwork（RBF-ANN）,least square-support vector machine（LS-SVM）,and linear discriminant analysis（LDA） were established to predict the species of the vegetable oils.The result demonstrated that the PCA obtained a clear clustering of objects respect to the species.RBF-ANN model performed better than the other two supervised pattern recognition models,with classification rate of 92.6%,and could predict the two component mixed oil sample accurately.The method could be used to distinguish the species of vegetable oil,and might be applicable for the identification of edible vegetable oils.