Knowledge of the full illuminant spectral power distribution is useful for many imaging applications. In most applications, however, accurate estimation is impossible because very few color measurements are made. In many of these cases, however, a great deal is known about the potential set of illuminants. In these cases, classification of scene illumination, rather than estimation of the full spectral power distribution of the illumination, is appropriate and useful. We analyze illuminant classification algorithms designed to group images by illuminant color temperature. To classify the illumination color temperature, a version of the correlation method suggested by Finlayson and colleagues is used. The original algorithm uses chromaticity coordinates, and thus does not use the fact that bright image regions contain more information about the illuminant than dark regions. Using calibrated images with known illuminants, we find that the original correlation method can be improved by using a scaled version of the red and blue sensor responses. When applied to these quantities, the algorithm is more sensitive to differences in illuminant color temperature. Then, we consider an application of the classification algorithm to the problem of rendering a color image acquired under one illumination under a second illuminant, with a different color temperature. This algorithm uses the ratio of R, G, and B sensor responses under different illuminants. The proposed method is applied to an image database of real scene.