Physical models for color image formation provide constraints which are useful for interpreting 3-D scenes. I summarize the physics underlying color image formation. Models for surface and body reflection from metals and dielectrics are analyzed in detail. This analysis allows us to evaluate the benefits we stand to gain by using color information in machine vision. I show from the reflection models that color allows the computation of image statistics which are independent of scene geometry. This principle has been used to develop an efficient algorithm for segmenting images of 3-D scenes using normalized color. The algorithm applies to images of a wide range of materials and surface textures and is useful for a wide variety of machine vision tasks including 3-D recognition and 3-D inspection. Experimental results are presented to demonstrate the scope of the models and the capabilities of the segmentation algorithm.