Texture is usually depicted by a gray-level distribution along with a certain spatial interaction. Gray level co-occurrence matrix(GLCM) is an appropriate candidate to depicted texture because of its capability of blending spatial interaction with gray-level distribution

thus

it can be widely applied in texture analysis. When calculating GLCM

the gray-level quantization would be needed in order to decrease matrix dimension

and certain information would be lose. A membership function matrix is established whereby the distance which between the real gray-level and the mean of quantization gray-levels area

and then

a newly co-occurrence matrix

namely fuzzy gray level co-occurrence matrix(FGLCM) is proposed. After appropriate features are selected based on FGLCM statistics properties analysis

the hidden markov model(HMM) classification is applied to divide the classical fracture surface image to four kinds. It is proved practically that FGLCM in this paper is better than the GLCM in depicting textures and the FGLCM combined with HMM is efficient performance in fracture surface images classification

and the recognition rate is 98%.