Flesh browning mostly happens in plum fruit during the post-harvest storage period, which is an important factor affecting the storage quality of plum fruits. Traditional methods used to discriminate plum browning involve the destruction of the intact fruit, which are highly subjective and error-prone. Therefore, the near-infrared (NIR) spectroscopy technique was applied to achieve rapid and non-destructive identification of plum browning and non-browning in this paper. The near infrared diffuse reflectance spectroscopy of 124 plum samples were collected in the band number of 4 000~12 500 cm-1. These samples were classified into two groups, browning (n=70) and non-browning (n=54). In order to find a new way to effectively discriminate plum fruits with flesh browning, three qualitative identification methods: the qualitative test, Mahalanobis distances discriminate analysis (DA) and Back Propagation-artificial neural networks (BP-ANN) were used to compare their capacity of recognizing browning plums and non-browning oneswhile the last two approaches were based on the principal component analysis (PCA) method. These results showed that DA and BP-ANN could be used to conctruct effective classification models for identifying plum browning, and the first ten principal components extracted from original spectra were applied as input variables to build DA and BP-ANN models. The optimal method was obtained with BP-ANN, which gained an accuracy of 100% for calibration set and 97.56% for prediction set, and the identification accuracy rate reached 100% and 98.57% for non-browning samples and browning ones, respectively. It could be concluded that NIR spectroscopy technique combined with chemometrics methods has great potential to recognize plums of browning and non-browning rapidly, non-destructively and effectively.