This work aimed to determine the anthocyanin content in skin based on hyperspectral imaging technology. The grapes of Cabernet Sauvignon (Vitis vinifera L.) produced in Shaanxi province were used as experimental materials. Hyperspectral images of 60 groups of grape samples were collected by near infrared hyperspectral camera (900~1700nm). After then, the anthocyanin content of skin was detected by pH-differential method. The grape berry regions of hyperspectral images were extracted as region of interest (ROI) in which its average spectrum was calculated. Moreover, different preprocessing methods were used to improve the signal noise ratio (SNR) including Savitzky-Golay smoothing, normalization and multiplicative scatter correction, et al. Prediction model was established for determining anthocyanin content by the partial least squares regression (PLSR), least squares support vector regression (SVR) and BP neural network (BPNN). It was shown that prediction coefficient of determination (P-R2) of BPNN model built by the thirteen latent variables recommended by PLSR model was 0.9102 and the root mean square error of prediction (RMSEP) was 0.3795. 