Variety discrimination for single rice seed is important to prevent the mixing and adulteration during seed production and to ensure the seed purity. A fast discrimination method for single rice seed by using the hyperspectral imaging technology is investigated. Hyperspectral images of rice seeds from ten varieties are collected over the wavelength region of 400~1000 nm, and the spectral, texture and morphological features of rice seeds are extracted. The discrimination accuracy of different features and their combinations is compared by using the partial least squares discriminant analysis, and the multiple progressive uninformative variable elimination algorithm combined with the partial least squares projection analysis algorithm is used for optimal waveband selection. The results show that the satisfactory discrimination accuracy, which is 99.22% and 96% for the training set and test set respectively, is achieved when mean, entropy and power features for the 23 optimal wavebands and morphological features are integrated. It suggests that multiple hyperspectral feature integration can effectively improve discrimination accuracy for single rice seed at the case of a small amount of wavebands, which basically meets the requirements of the national standards on the seed purity identification.