The reflectance spectra of 4 common oil types, kerosene (with the thickness of 300，500 and 1 000 μm), lubricating oil (with the thickness of 300, 1 000, 1 500 μm), light diesel oil (with the thickness of 50, 300, 500 μm) and 180# diesel ( with the thickness of 500 and 2 000 μm) were analyzed by using cluster analysis and principal component analysis (PCA), in order to explore a fast, timely method for oil type identification. The results of cluster analysis showed that: when the cluster distance between samples was calculated by Euclidean distance and when the distance L=8.976 samples could be correctly classified, the accuracy was up to100%; it also showed the thickness of oil film affected the clustering effects; the principal component analysis showed that: the PCA scores of wavelet detail coefficients had the best result among the original data, the wavelet approximate coefficients and detail coefficients. The methods of using spectral reflectance data combined with cluster analysis and the principal component analysis based on wavelet detail coefficients to identify the type of water film are feasible.