The Euclidean distance measure has been used in comparing feature vectors of images, while the cosine angle distance measure is used in document retrieval. We theoretically analyze these two distance measures based on feature vectors normalized by image size and experiment with them in the context of a color image database. We find that the cosine angle distance, in general, works equally well for image databases. We show, for a given query vector, the characteristics of feature vectors that will be favored by one measure but not by the other. We compute k-nearest neighbors for query images using both Euclidean and cosine angle distance for a small image database. The experimental data corroborate our theoretical results.