In the SIMCA, the parameters of PCA model and F test are used to construct T2 and Q for classification, and Euclidean distance is used to determine the range of sample distribution of the model. Since the range which is defined by Euclidean distance is a circle in the plane of T2 vs Q, the boundary of actual samples which distributes in some directions and irregular space cannot be presented accurately. Besides, SIMCA is still inaccurate for classification and identification in theory. Therefore, a new multivariate classification and identification method was proposed using Mahalanobis Distance instead of Euclidean distance in this paper. Experiments of infrared spectra of blending edible oils and near infrared spectra of animal furs were designed to compare the performance of the new method and SIMCA. The recognition rates of the new method and SIMCA for three kinds of furs are 85.5% and 75%, respectively. The recognition rates of the new method and SIMCA for two classes of blending edible oils are 65% and 55%, respectively. It has shown that the new method is superior to SIMCA in the performance of discriminating the different materials with a small difference in their chemical composition.