Rice is the main food crop for human beings, whose germination rate is one of the most important indexes to evaluate rice quality. The germination rate of brown rice named Nan Jing 46 is predicted by using hyperspectral imaging system. Hyperspectral images of 960 samples which are full and not moldy are captured, and the spectral region is from 400 nm to 1000 nm. The mean spectra are extracted from the region of interest of each image and principal component analysis (PCA) is applied to select characteristic wavelengths from the full- spectrum. The prediction models are established based on spectrum data of characteristic wavelengths of different rice parts using 4 prediction methods, including partial least squares (PLS), radial basis function neural network (RBFNN), general regression neural network (GRNN) and back-propagating neural network (BPNN). After repeated tests, the top area of brown rice (containing the germ) is chosen as the characteristic part, which has the best prediction performance ( R_p =0.970). The order of the prediction accuracy from low to high is PLS, BPNN, RBGNN, GRNN. Among these methods, GRNN has the highest prediction accuracy ( R_p=0.982, f_RMSEP =0.978). The results indicate that it is feasible to detect the germination rate of brown rice by the hyperspectral imaging system.