A key aspect of image retrieval using color, is the creation of robust and efficient indices. In particular, the color histogram remains the most popular index, due primarily to its simplicity. However, the color histogram has a number of drawbacks. Specifically, histograms capture only global activity, they require quantization to reduce dimensionality, are highly dependent on the chosen color space, have no means to exclude a certain color from a query, and can provide erroneous results due to gamma nonlinearity. In this paper, we present a vector angular distance measure, which is implemented as part of our database system. Our system does away with histogram techniques for color indexing and retrieval, and implements color vector techniques. We use color segmentation to extract regions of prominent color and use representative vectors from these extracted regions in the image indices. We therefore reach a much smaller index, which does not have the granularity of a histogram. Rather, similarity is based on our vector angular distance measure, between a query color vector and the indexed representative vectors.