In this paper, we propose a novel feature extraction scheme for texture classification, in which the texture features are extracted by a two-level hybrid scheme, by integrating two statistical techniques of texture analysis. In the first step, the low level features are extracted by the Gabor filters, and they are encoded with the feature map indices, using Kohonen's SOFM algorithm. In the next step, the encoded feature images are processed by the Gabor filters, Gaussian Markov random fields (GMRF), and Grey level co- occurrence matrix (GLCM) methods to extract the high level features. By integrating two methods of texture analysis in a cascaded manner, we obtained the texture features which achieved a high accuracy for the classification of texture patterns. The proposed schemes were tested on the real microtextures, and the Gabor-GMRF scheme achieved 10 percent increase of the recognition rate, compared to the result obtained by the simple Gabor filtering.