Most algorithms for extracting illuminant chromaticity from arbitrary images, such as the images found on the web, are based on machine learning techniques. We will show how a physics-based methodology can be adapted to provide relative illumination information on real images. More specifically, we use the inverse-intensity chromaticity representation and show how the analysis of the histograms of illuminationchromaticity candidates provides information about the type of illumination(s) present in a scene. Experiments indicate that the estimate is quite robust towards noise, and that simple measurements on the histogram peak can be used to countercheck the reliability of the estimate.