In the present work, the combination of attenuated total reflectance-Fourier transform infrared spectrometry (ATR-FTIR) and pattern recognition,including principal components analysis (PCA) and hierarchical cluster analysis(HCA), is used as a fast and convenient analytical tool to classify oil samples.Twenty five samples including crude oils and fuel oils with different totalcontents of n-alkanes were analyzed. It was found that multiplicative scattercorrection (MSC) and continuous wavelet transform (CWT) as a pretreatment methodcould improve the classification results of pattern recognition. Theclassification results were proved to be in agreement with the origin of the oilsamples. The oils with high content of n-alkanes and those with low content wereclassified clearly by this developed method, but it still had some constraint todifferentiating oils with little difference. The present work provides a feasiblemethod for quick classification of oils, which can be used for the initialidentification of spill oils and afford useful information for the furtheridentification of the oils.infrared spectrometry (ATR-FTIR); Principal components analysis (PCA);Hierarchical cluster analysis (HCA)验室课题项目(200816)资助