An efficient histogram analysis algorithm is proposed for real-time automated fruit surface quality evaluation. This approach, based on short-wave infrared imaging, provides excellent image contrast between the fruit surface and delaminated skin, which allows significant simplification of image processing algorithm and reduction of computational power requirements. The proposed method employs a very efficient training procedure to produce a normalized gray scale histogram and its corresponding skin threshold for each quality class. By histogram comparison, the test fruit is assigned to one of the four quality classes and an adaptive threshold is calculated for segmenting skin delamination areas from the fruit surface. The final quality grade is determined according to the fruit size and the percentage of delaminated skin. Experiment was performed in a packing facility in Arizona, USA. Testing results show the proposed method achieves 95-98% grading accuracy for different grades. Although this paper uses Medjool dates as an example to demonstrate the performance of the proposed method, it is suitable for and can be easily adapted to other fruit or vegetable grading applications. The proposed method has been implemented and used for commercial production for date quality evaluation. (C) 2014 Elsevier Ltd. All rights reserved.