This paper presents a comparative study of different distance measures used to compare histograms in applications such as pattern recognition, feature selection, image sorting, grouping, identification, indexing, and retrieval. The focus of the study is on how distance measures are affected by variations across images. Different distances between histograms were investigated and tested to compare their performance in retrieving gray scale and color images. A wide range of review papers on calculating distances between histograms was examined. One comparative study was found where histogram bins having zero value were discarded in the calculus of certain distances. We show that this is an inappropriate approach; our tests revealed that zero-value bins should be included to avoid erroneous calculations and achieve a performance advantage over other distance measures.