In order to achieve non-destructive variety identification of seabuckthorn juice,a fast discrimination method based on visible-near infrared reflectance (NIR) spectroscopy was put forward. A Field Spec 3 spectroradiometer was used for collecting 40 sample spectral data of three varieties of seabuckthorn juice separately. Average smoothing method and multiplicative scattering correction (MSC) method were used to complete the pretreatment of sample data. Then principal component analysis (PCA) was used to process the spectral data after pretreatment. A total of 120 seabuckthorn juice samples were divided into calibration set and validation set randomly,the calibration set had 90 samples and validation set had 30 samples. Eight principal components (PCs) were selected based on accumulative reliabilities which would be taken as the inputs of the three-layer back-propagation neural network,and seabuckthorn juice varieties were selected as the outputs of back propagation (BP) neural network. Then this model was used to predict 30 samples in the validation set. The result showed that a 100% recognition ratio was achieved with the threshold predictive error ±0.1. It could be concluded that PCA combined with BP neural network was an available method for varieties recognition of seabuckthorn juice based on NIR spectroscopy.