An integrated framework for content-based indexing and retrieval in video databases is presented in this paper, which has the capability of adapting its performance according to user requirements. Video sequences are represented by extracting a small number of key frames or scenes and constructing multidimensional feature vectors using fuzzy classification of color, motion or texture segment properties. Queries are then performed by employing a parametric distance between feature vectors, and adaptation is achieved by estimating distance parameters according to user requirements, resulting in a content based retrieval system of increased performance and flexibility.