Morphological characteristic of maize seed is an important factor in identifying maize varieties. Hyperspectral images of 432 maize seeds including nine varieties were acquired using hyperspectral imaging system. The images were corrected and preprocessed, and then shape features of each sample were extracted in the range of 563.6~911.4 nm including 55 wavelengths. The classification models were developed using the shape features of maize seeds from singlewavelength, multiwavelengths and full wavelengths coupled with partial least squares discriminant analysis (PLSDA), respectively. Simulation results indicate that the average correct identification rate of training set and testing set with full wavelengths is 98.31% and 93.98%, which are better than singlewavelength and multiwavelengths. Therefore, that is the accurate mean for identifying maize varieties using the feature information of visible and nearinfrared region from hyperspectral images.