Abstract: Gray level co-occurrence matrix (GLCM) is widely used for the description of different textures because of its advantage of representing the texture structure. In order to hold the multiresolution property and spectrum information simultaneously, GLCM is often computed from each wavelet subband, which, however, leads to much higher dimension of feature. To overcome this problem, a new feature extracting algorithm is proposed named multiresolution co-occurrence matrix (MCM), whose feature vector has a comparative low dimension. For the task of dimension reduction, though the MCM is computed from each subband of undecimated wavelet transformation as GLCM does, the parameters for the MCM are well chosen taking into consideration the scale and orientation of wavelet subbands. In addition, the feature dimension of the MCM can be further reduced through a new feature selection method based on the correlation analysis of inter-subbands and intra-subbands. Performance analysis is made in detail for the statistics of the MCM and wavelet energy feature. The proposed MCM is measured through the classification test on the Brodatz album. Experimental results demonstrate that MCM statistics outperforms other methods such as wavelet energy, GLCM, and the catenation of these two features. Extensive experiments on feature selection show the effectiveness of the proposed method, which can reduce the dimension successfully without any loss of classification performance.