Digital Still Cameras employ automatic white balance techniques to adjust sensor amplifier gains so that white imaged objects appear white. A color cast detection algorithm is presented that uses histogram and segmentation techniques to select near-neutral objects in the image. Once identified and classified, these objects permit determination of the scene illuminant and implicitly the respective amplifier gains. Under certain circumstances, a scene may contain no near-neutral objects. By using the segmentation operations on non-neutral image objects, memory colors, from skin, sky, and foliage objects, may be identified. If identified, these memory colors provide enough chromatic information to predict the scene illuminant. By combining the approaches from near-neutral objects with those of memory color objects, a reasonable automatic white balance over a wide range of scenes is possible.