Many different varieties of rice look very similar, but their chemical composition and final product quality vary greatly, which causes huge economic losses each year as a result of variety confusion. Identification of rice varieties is the practical requirement for developing high quality grain engineering. In this paper, a fast and non-destructive method for rice variety identification using hyperspectral imaging technology was proposed. The main research contents and results were as follows: (1) Average spectrawere extracted from the region of total 150 samples with wavelength from 388～1 000 nm. In the full band, the reflectance was most obvious at 600～800 nm, which was calculated by Stacked stacking and curve-smoothing for increasing its differences. (2) Principal component analysis (PCA) was used to analyze the reflectance data smoothed. It was found that the wavelength with the largest weight coefficient was located at 680 nm and used as the characteristic wavelength. Loading the texture image of the characteristic wavelengths, the texture characteristic parameters of each rice sample were calculated as follows: Mean, Variance, Entropy and Skewness. Meanwhile, the thresholding method was used to separate the target from the background, and the morphological parameters of each grain werecalculated as follows: areas/pixels2, perimeter/pixels, length of long axis/pixels, length of short axis/pixels. Based on the texture characteristics and morphological characteristics, the Fisher discriminant analysis model, partial least squares regression (PLSR) mode and Artificial neural network model (ANN) were established respectively for rice variety identification. (3) The results showed that the cumulative variance contribution rate of function 1 and function 2 established by Fisher discriminant analysis reached 93%, which could better explain the rice variety information. Comparing the function value of the sample with the square Mahalanobis distance of the group centroid, the individuals with similar values were taken as the same category. The overall recognition accuracy of the five rice varieties could reach 95.3%. The PLSR model: Yvarieties=0.03Xmeans-0.36Xvarious-0.24Xentropy+0.37Xskewness+0.31Xarea-0.32Xperimeter-0.39Xlength of long axis+0.45Xlength of short axis, with correlation coefficient (r)=0.98, corrected root mean square (RMESS)=0.29, cross validation root mean square (RMESSCV)=0.32, the accuracy of rice varieties identification could reach 95%. The neural network model is a two-layer feedforward network with sigmoid hidden and soft max output neurons, which randomly divides 150 samples into training samples, validation sets and test sets according to the ratio of 70%∶15%∶15%. With training algorithm of conjugate gradient method and evaluation index of Cross-Entropy method, the accuracy of rice variety identification can reach 98%. The overall results show that the neural network model of rice variety identification is superior to Fisher discriminant and PLSR in classification accuracy, which has an important guiding significance for rapid and non-destructive identification of rice varieties.