Although objects can be identified based solely on the information provided by the spatial structure of an image, color adds another perceptual dimension which may facilitate object identification. Here, we ask whether color in natural images is associated with improvements in object recognition performance. We used a procedure based on Fourier analysis to systematically degrade colored and achromatic natural images. Both stimulus types were mixed with increasing amounts of achromatic noise, leading to loss of spatial structure in the images. At a given noise level, the difference between colored and achromatic images was thus provided only by the remaining color. We used a delayed matching to sample paradigm, where a sample stimulus was presented (250ms), followed by a probe stimulus after a delay period (1s). Sample stimuli were presented at various noise levels, whereas probe stimuli were always undegraded. A lever press was required if the sample stimulus matched or was a degraded version of the probe stimulus. We have preliminary results from one monkey. The monkey’s recognition performance decreased as a function of noise level for both color and achromatic conditions. In addition, we found that the recognition performance was significantly higher for the color condition than the achromatic condition at the same degradation level (p= 0.0303, N = 5). Since the spatial information for both stimulus versions is equally degraded by the visual noise, these results suggest that color, independently of spatial composition, is associated with an advantage in object identification performance.