The efficiency of texture image classification is certainly influenced by image scale when a feature space or a classification method is not scale invariant. An alternative approach to the scale-invariant techniques is proposed that first estimates an effective image scale and then uses it to adjust texture features to get the best possible texture image recognition and classification. We use the correlation distance between pixels as a measure of the scale of texture images. We study the performance of classification of texture images in the coordinated cluster representation (CCR) versus an image scale and the size of the scanning window used for the coordinated cluster transform. Given the number of classes to be classified in, we find that an optimal (up to 100%) classification efficiency in the CCR feature space is obtained by changing an image scale and/or the size of the scanning window in the coordinated cluster transform.