In order to study the feasibility of the computerized grading system of pears with rich spots, large number of pears are put in a light box, where images of different aspects of them are captured by cameras, which are connected to the computer. Additionally, two methods are proposed to remove the spots on the surface of pears in the image to reduce their effect on defect detection. The advantages and disadvantages of the two methods are discussed. One method works by applying an adaptive threshold and the other works by combining filtering with edge detection. In reference to the National Standard of China, grading based on shape and defect is studied. An ANN (artificial neural network) model, which colligates the information of shape, color, and defect, is established to grade the pears comprehensively. The method of the adaptive threshold has a better effect when it is used in processing pears with rich spots, such as Laiyang pear, whereas its executive efficiency is slightly lower. Yet, to the species on which the spots are not so conspicuous, such as the Huangjin pear and the Fengshui pear, the method that combines filtering with edge detection is supposed to be applied for better performance. The results of the grading by ANN model based on shape, defect, and comprehensive quality, respectively, reach the accuracy of 87.5%, 92.6%, and 90.3%. The methods proposed to remove spots and the model constructed for grading and recognition have positive significance to grading pears with rich spots by appearance.