Abstract: In view of corn leaf disease image characteristics, one multi-classification machine is applied in corn leaf disease recognition. First the algorithm of live-ware segmentation was used to find disease part and the algorithm of the wavelet feature extraction was used to make the corn disease leaf the characteristic vectors, then the support vector machine classification method was applied to recognize the disease. The corn leaf disease image recognition experiment indicates that Support Vector Machine classification method suits the small sample situation and has the better classification ability. The method suits corn leaf disease classification. The different classification kernel functions are compared, and analysis shows that the radial base function most suits the corn leaf disease classification recognition.