Chemometric analysis was used to classify different oils based on the m/z 256 mass chromatogram. Three hundred oil samples comprised of 12 LFOs, 104 HFOs (13 weathered fuel oils), and 184 crude oil samples (63 weathered crude oils) were analyzed by GC-MS, and the m/z 256 mass chromatogram was chosen for classification. After normalization, the m/z 256 mass chromatograms were aligned using the correlation optimized warping (COW) method with segment lengths of 50 data points and slack parameters of 4 data points. They were then analyzed by principal component analysis (PCA). The score graphs of the PCs revealed that there was a good discriminant for HFOs, LHOs and oils. According to the changing tendency of the correct sample percentage, five PCs were chosen for the linear discriminant analysis (LDA). A good model with three discriminant equations for HFOs, LFOs and oils was obtained. For the training set with 172 samples, the correct percentage reached 100%, and 99% was obtained for the test set with 128 samples. This study proved that the COW-PCA-LDA method based on the m/z 256 mass chromatogram can be used in oil fingerprinting identification, especially for oil type classification.