Huanghua pear is an important fruit in China. The shape and condition of the stems are important indices for classifying Huanghua pears. Images of Huanghua pears were acquired with a machine vision system. Using templates with different sizes, an algorithm for judging the presence of stems was developed. Meanwhile, the stem head and the joint point between the stem and the pear body were labeled. After calculating slopes of the approximate tangential lines of the stem at the head and bottom positions, the included angle of these two lines was obtained. It was found that the included angle of a broken stem was smaller than that of a good stem. Based on this feature, good stems can be distinguished from broken stems. Results of a test on 53 pear images showed that the accuracy for judging the presence and integrity of the stems reached 100% and 93%, respectively. A method for describing the irregular shapes of Huanghua pears was also studied. Fourier transformation and Fourier inverse transformation pairs were used as the shape descriptors. The first 16 harmonic components of the Fourier descriptor were found sufficient to represent the primary shapes of uanghua pears. These components were used as the inputs to an artificial neural network (ANN) to classify Huanghua pears. The classification accuracy reached 90%.