Query-by-content image database will be based on similarity, rater than on matching, where similarity is a measure that is defined and meaningful for every pair of images in the image space. Since it is the human user that, in the end, has to be satisfied with the results of the query, it is natural to base the similarity measure that we will use on the characteristics of human similarity assessment. In the first part of this paper, we review some of these characteristics and define a similarity measure based on them. Another problem that similarity-based databases will have to face is how to combine different queries into a single complex query. We present a solution based on three operators that are the analogous of the and, or, and not operators one uses in traditional databases. These operators are powerful enough to express queries of unlimited complexity, yet have a very intuitive behavior, making easy for the user to specify a query tailored to a particular need.