This paper assesses the usefulness of color information for face recognition tasks. Experimental results using the FERET database show that color information improves performance for detecting and locating eyes and faces, respectively, and that there is no significant difference in recognition accuracy between full color and gray-scale face imagery. Our experiments have also shown that the eigenvectors generated by the red channel lead to improved performance against the eigenvectors generated from all the other monochromatic channels. The probable reason for this observation is that in the near infrared portion of the electro-magnetic spectrum, the face is least sensitive to changes in illumination. As a consequence it seems that the color space as a whole does not improve performance on face recognition but that when one considers monochrome channels on their own the red channel could benefit both learning the eigenspace and serving as input to project on it.