Color descriptors have been used extensively and successfully in many computer vision applications. However, object colors are sensitive to changes in illumination conditions such as lighting geometry and illumination color. Changes in the illumination of a scene can greatly affect the performance of image matching and object recognition if the color descriptors used are not robust to these changes. This study examined and compared the invariance properties of various state-of-the-art color descriptors and evaluated their performance on image matching under changing illumination conditions. We evaluated the performance of the color descriptors on image matching under changing illumination conditions using the Amsterdam Library of Object Images (ALOI) database, which is an image database of colored objects taken under various imaging conditions. Experimental results indicate that SIFT-based descriptors and LBP-based descriptors are less sensitive to changes in illumination conditions. Comparing to other color descriptors, the SIFT-Opponent descriptor has the best overall performance. The Opponent color descriptor, on the other hand, has the lowest correct match rates.