Color images often must be color balanced to remove unwanted color casts. Color balancing uncalibrated images (e.g. downloaded from the Internet or scanned from an unknown film) adds additional challenges to the already difficult problem of color correction because neither the pre-processing to which the image was subjected, nor the camera sensors or camera balance are known. In this article, we propose a framework for dealing with some aspects of this type of image. In particular, we discuss the issue of color correcting images where an unknown ‘gamma’ non-linearity may be present. We show that the diagonal model, used for color correcting linear images, also works in the case of gamma corrected images. We also discuss the influence that unknown camera balance and unknown sensors have on color constancy algorithms. To perform color correction on uncalibrated images, we extend previous work on using a neural network for illumination, or white-point, estimation from the case of calibrated images to that of uncalibrated images of unknown origin. The results show that the chromaticity of the ambient illumination in uncalibrated images can be estimated with an average CIE Lab error around 5ΔE. Comparisons are made to the grayworld and white-patch methods.