Surface texture is an important characteristic for the analysis of many types of images. This is particularly true in the case of mechanical surfaces. An image texture is described by the number and types of its primitives or layout of its primitives. Image texture can be qualitatively evaluated as having one or more of the properties of fineness, coarseness, smoothness, granulation and irregularity. A solution to the texture analysis problem will greatly advance the image processing fields and it will also bring much benefit to many applications in the areas of industrial automation and remote sensing. In this paper, a machine vision system has been utilized to capture the images and then the surface textural features of the machined surfaces (grinding, milling and shaping) are extracted using the most widely used statistical methods, viz. Co-occurrence matrix, Run length matrix, Texture spectrum and Autocorrelation. The features calculated from these matrices are correlated with surface parameters, such as roughness and the different features are studied for classification of these surfaces.