Color image variations produced by the change of video acquisition devices strongly affect color appearance based recognition tasks in digital video applications. Up to date, many algorithms have been developed in order to achieve independence from different image formation factors like intensity, orientation and color of the illumination source. In this paper, we check the capability of three of these approaches to generate device independent color representations in order to be used as a normalization step before the recognition process. Our recently developed color correction approach for video imagery based on Gaussian mixture models has also been tested. Experimental results show that a simple algorithm as if the gray world correction is enough for appearance based recognition under this particular kind of color variability. Similar recognition rates are obtained using our correction, with the advantage that the visual quality of normalized images is preserved. In digital video library applications, where not only recognition tasks are performed, this approach becomes much more useful.