Discriminant models of adulterated milk and pure milk were established using BP neural network combined with two-dimensional (2D) correlation near-infrared spectra parameterization. Forty pure milk samples, 40 adulterated milk samples with urea (1～20 g·L-1) and 40 adulterated milk samples with melamine (0.01～3 g·L-1) were prepared respectively. Based on the characteristics of 2D correlation near-infrared spectra of pure milk and adulterated milk, 5 apparent statistic parameters were calculated based on the parameterization theory. Using 5 characteristic parameters, discriminant models of urea adulterated milk, melamine adulterated milk and two types of adulterated milk were built by BP neural network. The prediction rate of unknown samples were 95%, 100% and 96.7%, respectively. The results show that this method can extract effectively feature information of adulterant, reduce the input dimensions of BP neural network, and better realize qualitative analysis of adulterant in milk.