The method is based on recognizing that certain local binary patterns, termed are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. The Local Binary Pattern (LBP) is a texture descriptor based on the probability of occurrence of elementary binary patterns (texels) defined over a circular window. A new feature set derived from the LBP, called the LBP-Constant-Symmetry (LBP-CS) and LBP-High-Symmetry (LBP-HS) are proposed for recognition of stone textures. The features are computed from each band of an isotropic color LBP Matrix for recognition. The tests were conducted in a variety of industrial samples. The obtained results are promising and show the possibility of efficiently recognizing complex industrial products based on color and texture features The Local Binary Pattern (LBP) approach has evolved to represent a significant breakthrough in texture analysis, outperforming earlier methods in many applications. Perhaps the most important property of the LBP operator in real-world applications is its tolerance against illumination changes. Another equally important is its computational simplicity, which makes it possible to analyze images in challenging real-time settings. Based on LBP approach the present study proposes a new method for the analysis of color stone textures. For this the present study evaluated co-occurrence features on patterns extracted from local binary operator. The present study also derived new statistical parameters called LBP constant and high symmetry. These parameters are also used in the color stone texture analysis. Image texture analysis is an important fundamental problem in computer vision. During the past few years, several authors have developed theoretically and computationally simple, but very efficient nonparametric methodology for texture analysis based on LBP (1, 2, 3, 4, 5, 6, 7, 8,9). The LBP texture analysis operator is defined as a gray scale invariant texture measure, derived from a general definition of texture in a local neighbourhood. For each pixel in an image, a binary code is produced by thresholding its value with the value of the centre pixel. A histogram is created to collect up the occurrences of different binary patterns. The basic version of the LBP operator considers only the eight neighbours of a pixel, but the definition has been extended to include all circular neighbourhoods with any number of pixels (10, 11, 12). Through its extensions, the LBP operator has been made into a really powerful measure of image texture, showing excellent results in terms of accuracy and computational complexity in many empirical studies. The LBP operator can be seen as a unifying approach to the traditionally divergent