Five different varieties of rice bought from the supermarket were identified with visual/near infrared spectroscopy (NIRS) technology. The spectra were collected by using ASD FieldSpec . 3 spectrometer, and 35 samples were obtained for each variety of rice. All the samples were divided randomly into two groups, one group with 150 ones used as calibrated set, and the other with 25 ones as validated set. Then the samples were analyzed with the whole wave band and the charac- teristic wave band(400～500nm, 910～1400nm and 1940～2300nm) models, respectively. The samples data were pre- treated by the methods of S.Golay smoothing and standard normal variable (SNV), and then the pretreated spectra data were analyzed with principal component analysis (PCA). The anterior 9 principal components computed by PCA were used as the input variables of back-propagation artificial neural network (BP-ANN) which included one hidden layer, while the values of rice varieties were used as the output variables of BP-ANN, and then the three layers BP-ANN discrimination model was built. The 25 unknown samples in the validated set were predicted by ANN-BP model. The results show that the discriminating rates are 100% in both models, and the accuracy of characteristic wave band model is higher than that of the whole wave band model. The results indicate that it is feasible to discriminate the variety and authenticity of rice by visible/ near infrared reflectance spectra as a rapid and non-contact way, and selecting characteristic wave band is one of the valida- ted methods to improve the precision of the discrimination model.