Many image clustering algorithms use distance metric in the process of taking decision. When dealing with color images, a distance metric will be used to decide whether two pixels or regions are closed. Colorimetric distances proposed by CIE(Commission Internationale de l'Eclairage) are often used in Lab color space because it is a uniform chromaticity space. However, RGB color space is useful to image processing and instead of converting color image from RGB to another color space before processing, it might be interesting to have the same or better results without changing the color space. In our work, we implement different distance metrics and compare the result of k-mean clustering algorithm in RGB color space to the one in L*a*b* with the colorimetric distance. Two evaluation criteria have been used and we conclude that being in RGB color space and choosing adequately the distance metric, we obtain better segmentation results.