To extract effectively characteristic information of adulterants in milk, the classification models for adulterated milk were established using two-dimensional(2D) correlation near-infrared spectra combining a Multi-way Principal Component Analysis(MPCA) with Least Square Support Vector Machines(LS-SVM). First, one-dimensional near-infrared spectra of pure milk and adulterated milk samples were collected and the synchronous 2D correlation spectra of all samples were calculated. Then, the MPCA was used to reduce dimension by extracting score matrix of 2D correlation data set. Finally, LS-SVM models for urea-tainted milk, melamine-tainted milk, and the above two kinds of adulterated milk were constructed by using score matrix extracted from 2D correlation spectra as the input variables. These models were used to discriminate the known samples in the test set and the results show that the classification accuracy rates of unknown samples are 923%, 96.2%, 92.3%, respectively. It demonstrates that the proposed method not only extracts effectively feature information of adulterants in milk, but also reduces the input dimension of LS-SVM and computational time. It realizes a better classification of adulterated milk and pure milk.