Abstract: Color constancy is an important perceptual ability of humans to recover the color of objects invariant of light information. It is also necessary for a robust machine vision system. Until now, a number of color constancy algorithms have been proposed in the literature. In particular, the edge-based color constancy uses the edge of an image to estimate light color. It is shown to be a rich framework that can represent many existing illumination estimation solutions with various parameter settings. However, color constancy is an ill-posed problem; every algorithm is always given out under some assumptions and can only produce the best performance when these assumptions are satisfied. In this article, we have investigated a combination strategy relying on the Extreme Learning Machine (ELM) technique that integrates the output of edge-based color constancy with multiple parameters. Experiments on real image data sets show that the proposed method works better than most single-color constancy methods and even some current state-of-the-art color constancy combination strategies.