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In this paper, we present a new approach for color texture classification by use of Haralick features extracted from co-occurrence matrices computed from local binary pattern (LBP) images. These LBP images, which are different from the color LBP initially proposed by Maenpaa and Pietikainen, are extracted from color texture images, which are coded in 28 different color spaces. An iterative procedure then selects among the extracted features, those which discriminate the textures, in order to build a low dimensional feature space. Experimental results, achieved with the BarkTex database, show the interest of this method with which a satisfying rate of well-classified images (85.6%) is obtained, with a 10-dimensional feature space.