The type of histogram distance metric selected for a CBIR query varies greatly and will affect the accuracy of the retrieval results. This paper compares the retrieval results of a variety of commonly used CBIR distance metrics: the Euclidean distance, the Manhattan distance, the vector cosine angle distance, histogram intersection distance, χ2 distance, Jensen-Shannon divergence, and the Earth Mover’s distance. A training set of ground-truth labeled images is used to build a classifier for the CBIR system, where the images were obtained from three commonly used benchmarking datasets: the WANG dataset (http://savvash.blogspot.com/2008/12/benchmark-databases-for-cbir.html), the Corel Subset dataset (http://vision.stanford.edu/resources_links.html), and the CalTech dataset (http://www.vision.caltech.edu/htmlfiles/). To implement the CBIR system, we use the Tamura texture features of coarseness, contrast, and directionality. We create texture histograms of the training set and the query images, and then measure the difference between a randomly selected query and the corresponding retrieved image using a k-nearest-neighbors approach. Precision and recall is used to evaluate the retrieval performance of the system, given a particular distance metric. Then, given the same query image, the distance metric is changed and performance of the system is evaluated once again.