Bees, like humans, can continue to see a surface from its color even when the scene's global illuminant changes (which is a phenomenon called color constancy). It is not known, however, whether they can also generate color-constant behavior in more natural complex scenes that are lit by multiple lights simultaneously, conditions in which most computational models of color constancy fail. To test whether they can indeed solve this more complex problem, bumblebees were raised in a highly controlled, yet ecological relevant environment consisting of a matrix of 64 artificial flowers under four spatially distinct lights. As in nature, the bees had no direct access to spectral information about the illuminants or flowers. Furthermore, the background of all of the flowers in the matrix was black, independent of illumination. The stimulus information presented to the bee was, therefore, far more constrained than that normally experienced in nature. And yet, bees learned to identify the rewarded flowers in each differently illuminated region of the matrix, even when the illumination of one of the regions was switched with one the bees had not previously experienced. These results suggest that bees can generate color-constant behavior by encoding empirically significant contrast relationships between statistically dependent, but visually distinct, stimulus elements of scenes.color ͉ vision ͉ color constancy ͉ context learning ͉ insect vision