Abstract: In this paper, computer vision, image processing and BP neural network were combined together to realize automatic identification of external quality of wheat grain. After comparing different background images, it was proved that images taken in a black flock paper background by a digital camera were the most preferable. An image segmenting software based on watershed algorithm was then designed to segment wheat images and identify each grain. For each wheat grain, 12 morphological characteristics and 12 color and luster parameters were calculated. With these 24 features, a wheat quality identification model was developed by applying BP artificial neural network. The model was employed to identify wheat quality. After times of modeling, it was proved that the final model was stable and repeatable. The average identification rate reached 93%.