Using a self-developed field imaging spectrometer system (FISS), hyperspectral images of 14 typical kinds of milk were acquired, based on which the discrimination of varieties of milk was studied. Firstly, removing 2 abnormal samples, the remaining 12 kinds of milk were randomly sampled, a total of 1 200 pixel samples. To eliminating high-frequency random noises and baseline offset and decrease the multi-collinearity, all samples were preprocessed by smooth-moving average and first derivative. Secondly, multiple discriminant analysis models for milk were built using characteristic wavelengths selected by the stepwise method. Results demonstrated that the overall identification accuracy for 1 200 spectral samples put together reached 95.5%, of which the overall distinguishing rate of Mengniu, Yili and Guangming acidophilous milk was 88.3%. The discriminant models for the three kinds of acidophilous milk subset, 300 spectral samples in all, were built, with the overall distinguishing rate of 88.7%. This explicated that FISS would be useful for discriminating milk varieties, and to accomplish specific discrimination of milk varieties, it would be best for milk of the same type from different manufacturers to form a subset, which may not only reduce the model variables, improving operational efficiency and the stability of the model, but improve their overall discriminant accuracy.