Abstract: Hyperspectral images of five corn varieties were acquired using Field Imaging Spectrometer System(FISS). After reflectance retrieved, noise removal and first-order differential, stepwise discrimination analysis based on the minimization of Wilks' lambda was employed to select the feature bands of corn spectral, and then discrimination model was built. The results of Least-one-out Cross-validation(loocv) show that:(1) average discrimination accuracy is 91.6%, in which, discrimination accuracy of High-oil corn No.115 is 87%, and discrimination accuracy of the other varieties is over 90%;(2) if discrimination method, band number and the size of samples of each variety are fixed, discrimination accuracy is effected by variety number and separable;(3) the effect of selected band number on discrimination accuracy is analysed and result shows that discrimination accuracy increases with the increasing of band number. Therefore, FISS has an important application value in corn-variety discrimination and quality examination.