In the field of image analysis in pattern recognition, shape is an important attribute to characterize graphical objects. It provides important information about an image. In the thesis, I proposed a new descriptor for image identification and classification, named Average Degree Descriptor. We did some experiments and compared its performance with Degree Descriptor. We also analyzed the Average Degree Descriptor theoretically, by comparing the data of distorted shapes and shapes of Kiki/Bouba. Since we also need to classify or identify some 3-dimension shapes in practical application, we proposed an approach to transform 3-dimension shapes to 2-dimension shapes. Moreover, we also studied the robustness of the proposed Average Degree Descriptor in random degradation. Results show that the proposed Average Degree Descriptor has good performance in image identification, even with random degradation.