The tea quality level was estimated with hyperspectral imaging technology. A hyperspectral imaging system based on spectrometer was developed to perform acquisition of hyperspectral imaging data. The principal component analysis (PCA) was performed to select three optimal bands images. Then, six texture features (i.e., mean, standard deviation, smoothness, third moment, uniformity, and entropy) based on the statistical moment were extracted from each optimal band image, thus 18 variables for each tea sample in all. Finally, PCA was performed again to compress 18 features variables, and 8 principal components (PCs) were extracted as the input of back propagation (BP) neural net. Experimental results showed that discriminating rate was 97% in the training set and 94% in the prediction set. Overall results sufficiently demonstrate that the hyperspectral imaging technology can be used to estimate tea quality level.