In order to explore rapid reliable Hebra cistanche detection methods, identification of 3 different sources of Hebra cistanche: cistanche deserticola, cistanche tubulosa, sand rossia is studied via fluorescent spectral imaging technology combined with pattern recognition. It is found in experiment that cistanche samples have obvious fluorescence properties. Forty fluorescence spectral images of 3 different sources of Hebra cistanche samples are collected through fluorescent spectral imaging system. After carrying on denoising and binarization processing to these images, the spectral curves of each sample was drawn according to the spectral cube. The obtained spectra data in the 450～680 nm wavelength range is regarded as the study object of discriminant analysis. Then, principal component analysis (PCA) is applied to reduce the dimension of spectroscopic data of the three kinds of cistanche and fisher distinction is used in combination to classify them; During the experiment were compared the effects of three methods of data preprocessing on the model: multiplicative scatter correction (MSC), standard normal variable correction (SNV) and first-order differential (FD) and then according to the cumulative contribution rate of the principal component and the effect of number of factors on the discriminant model to optimize the number of principal components factor. The results showed that: identification of the best after the first derivative pretreatment then the first four principal components is extracted to carry on fisher discriminant, discriminant model of 3 different sources of Hebra cistanche is set up through PCA combined with fisher discriminant the precision of original discrimination is 100%, recognition rate of the cross validation is 95%. It was thus shown that the fluorescent spectral imaging technology combined with principal components analysis and fisher distinction can be used for the identification study of 3 different sources of Hebra cistanche and has the advantages of easy operation, speediness, reliability.