A novel method for fast recognition of gasoline brands based on the Raman spectroscopy is presented. A classification model on the basis of product gasoline samples with known brands was established. The detailed modeling process includes measurement and pretreatment of Raman spectra of these samples, principal component analysis (PCA) to obtain loading vectors and score vectors of all known samples, and calculating each average score vector for all of the samples with the same brand. For a gasoline sample with unknown brand, first measure and preprocess its Raman spectrum with the same pretreatment algorithm, then calculate its score vector on the above loading vectors and its distances to the average score vectors for different brands, and finally determine the brand of the unknown sample by the minimum distance. For 45 product gasoline samples from different refinery, experimental results show that there are significant distances between different brands in the principal component space, and the above classification model can decide the brand of unknown gasoline samples rapidly and accurately.