In many machine vision applications, objects or scenes are imaged in color (red, green and blue) but then transformed into grayscale images before processing. One can use equal weights for the contribution of the color components to gary scale image or can use the unequal weights provided by the luminance mapping of the National Television Standards Committee (NTSC) standard. NTSC weights, which basically enhance the visual properties of the images, may not perform well for classification purposes. In this study, we propose an adaptive color-to-grayscale conversion approach which increases the accuracy of the image classification problems. The method optimizes the contribution of the color components which increases the between-class distances of the images in opponent classes. It's observed from the experimental results that the proposed method increases the distances of the images in classes between 1% and 87% depending on the dataset which results increases in classification accuracies between 1% and 4% on benchmark classifiers.