Based on the near-infrared spectra (NIRS) of the measured samples as the discriminant variables of their genotypes, the genotype discriminant model of SNP has been established by using back-propagation artificial neural network (BP-ANN). Taking a SNP (857G > A) of N-acetyltransferase 2 (NAT2) as an example, DNA fragments containing the SNP site were amplified by the PCR method based on a pair of primers to obtain the three-genotype (GG, AA, and GA) modeling samples. The NIRS-s of the amplified samples were directly measured in transmission by using quartz cell. Based on the sample spectra measured, the two BP-ANN-s were combined to obtain the stronger ability of the three-genotype classification. One of them was established to compress the measured NIRS variables by using the resilient back-propagation algorithm, and another network established by Levenberg–Marquardt algorithm according to the compressed NIRS-s was used as the discriminant model of the three-genotype classification. For the established model, the root mean square error for the training and the prediction sample sets were 0.0135 and 0.0132, respectively. Certainly, this model could rightly predict the three genotypes (i.e. the accuracy of prediction samples was up to100%) and had a good robust for the prediction of unknown samples. Since the three genotypes of SNP could be directly determined by using the NIRS-s without any preprocessing for the analyzed samples after PCR, this method is simple, rapid and low-cost.