A new method for fast discrimination of varieties of corn by means of near infrared spectroscopy and biomimetic pattern recognition (BPR) was proposed and the recognition models for seven kinds of corn were built. The experiment adopted 140 samples acquired from seven varieties of corn. Firstly, a field spectroradiometer was used for collecting spectra in the wave number range of 4 000 to 12 000 cm-1. Secondly, the original spectral data were pretreated in order to eliminate noise and improve the efficiency of models, and then the characteristic spectral regions were selected by using fixed-sized moving window evolving factor analysis. Thirdly, principal component analysis (PCA) was used to compress spectral data into several variables, and the cumulate reliabilities of the first five components were more than 99.96%. Finally, according to the first five components, the recognition models were established based on BPR. For the samples in each variety, 10 samples were randomly selected as the training set. The remaining samples of the same variety were used as the first testing set, and the 120 samples of the other varieties were used as the second testing set. Under the condition that almost all the samples in the second set were correctly rejected, the average correct recognition rate was 94.3%. The experimental results demonstrated that the recognition models were effective and efficient. In short, it is feasible to discriminate varieties of corn based on near infrared spectroscopy and BPR.