The recognition of wood species is needed is many a reas like construction industry, furniture manufacturing, etc.,. The wood is traditionally cla ssified by human experts. But human identification of wood type is not accurate and the manual identifica tion is a time consuming process. So in this study, an intelligent recognition for identification of wo od species was developed. This study uses image enhancement as a preprocessing techniques and uses a new method which divides the image into several blocks known as image blocking. Each block is extracted using grey image and edge detection techniques. The Grey-Level Co-occurrence Matrix (GLCM) is used as a texture classification technique. The GLCMs are generated to obtain three features: Entropy, standard deviation and correlation. The classification technique used to c lassify the wood species is correlation. Our experimental results showed that the proposed metho d can increase the recognition rate up to 95%, which is faster and better than existing system whi ch gives 85% recognition rate.