To address the limitations of conventional techniques, a method of principal component analysis and BP neural network was discussed to diagnose and classify citrus HuangLongBing. Data was obtained by a hyperspectral imaging system with the wavelength range of 370~988 nm, its high dimension data was reduced by principal component analysis, and then BP neural network was used to model for classification. The results showed that the first four principal components cumulative variance contribution rate achieved 97.42%. On one hand, BP neural network classification accuracy rate achieved 85% or more; on the other hand, after the principal component analysis, classification of BP neural network accuracy substantially was more than 90%. This method for nondestructive testing of citrus HuangLongBing is feasible.