This research investigates the use of image content analysis techniques as a tool of understanding image content, organizing image databases and choosing appropriate low level features as well as semantic meanings for image indexing and retrieval. An intelligent image indexing and query system consisting of semantic classification, composite indexing and interactive query is proposed. In this system, a large collection of images with great varieties is analyzed by the content and categorized into different classes according to distinct characteristics. The semantics of feature descriptors and the relationship between feature descriptors and image contents are then explored. Finally, a composite indexing and interactive retrieval procedure using best low-level features and high-level understanding is developed to achieve a robust image query performance.