SIMCA pattern recognition methods have been applied to mass spectral data from a target list of hazardous chemicals. A scheme has been proposed for classification and identification of five classes of compounds including aromatics, chlorocarbons, bromocarbons, hydrocarbons and polychlorinated biphenyls (PCBs). In addition, partial least squares regression has been used to predict the number of chlorine atoms present in the PCBs. A training set of 429 compounds was used. Classification models were derived by using principal components analysis of the autocorrelation transformed spectra and were-applied to two gas chromatography-mass spectrometry ambient air field samples and one hazardous waste sample. An optimal number of three principal components was determined. For identification the mass spectra of the unknowns were compared with the training set mass spectra in the predicted class. The overall classification and identification rates for training spectra were 89% and for the unknowns were 92% and 82%, respectively. Identification results of the SIMCA scheme for an ambient air field sample also was compared to that of probability based matching (PBM). Correct identification for the spectra in the sample by SIMCA was 71/84 (85%) as opposed 35/84 (42%) by PBM.