The texture is an essential characteristic of the image. So, recognition of texture is increasingly becoming a major topic in many image processing applications such as image retrieving, image classification, similarity, object recognition, and detection. The recognition of texture tries to allocate an unidentified image to one of the identified class of textures. This paper proposes a novel feature extraction technique for classification and recognition of color texture. The significant advantage of the introduced method is that it combines the extraction of local and global features of the color texture by using Local Binary Pattern (LBP) and multi-channel orthogonal radial substituted Chebyshev moments, respectively. Relevant features (local or global) provides discriminatory information that used to differentiate one object from another. Global features represent the image as a whole, while local features represent a specific part of the image. We performed experiments using challenging datasets: (Outex, ALOT) to test the efficacy of our image classification descriptors. The result of this approach has said that our descriptor is valid, competitive, discriminatory, and exceeds the current state-of-art methods.