Recent research on image databases has been aimed at the development of content-based retrieval techniques for the management of visual information. Compared with such visual information as color, texture, and spatial constraints, shape is an important feature. Associated with those image objects of interest, shape alone may be sufficient to identify and classify an object completely and accurately. This paper presents a novel method, based on feature point histogram indexing for object shape representation in image databases. In this scheme, the feature point histogram is obtained by discretizing the angles produced by the Delaunay triangulation of a set of unique feature points, which characterize object shape in context, and then counting the number of times each discrete angle occurs in the resulting triangulation. The proposed shape representation technique is translation, scale, and rotation independent. Our various experiments concluded that the Euclidean distance performs well as the similarity measure function, in combination with the feature point histogram computed by counting the two largest angles of each individual Delauney triangle. Through further experiments, we also found evidence that an image object representation, using a feature point histogram, provides an effective cue for image object discrimination.