Traditional database systems only support textual and numerical data. Video data stored in these database systems can only be retrieved through their video identifiers, titles or descriptions. In the video data, frame-by-frame object change is one of the most obvious information. Each video contains temporal and spatial relationships between content objects. The temporal relationships can be specified between frame sequences and the spatial relationships can be specified by the relationships between objects in a single frame. The difficulty in designing a content-based video database system is how to store and describe the relationships between moving objects completely. Many researches on content-based video retrieval represented the content of video as a set of frames, but they either left out the temporal ordering of frames in the shot or only stored the relationships between objects in a single frame. According to these observations, we conclude that a content-based video database system requires video indexing, query processing and a convenient user interface to fit the requirements and characteristics of videos. In this thesis, we design and implement a query processing strategy for video data. In the proposed strategy, we consider three query types: the exact object match, the spatial-temporal object retrieval and the motion query, where a exact object match is to find the video files which contain the specific objects, a spatial-temporal objects retrieval is to retrieve the object pairs that satisfy some spatial-temporal relationships and a motion query is to find the set of frames which contain the object movements. Moreover, we consider three design issues: the video indexing, the video query processing and the video query interface. When there are a large number of videos in a video database and each video contains many shots, frames and objects, the processing time for content retrieval is tremendous. Thus, we need a proper video indexing strategy to speed up the searching time. In order to fulfill the spatial-temporal relationships of objects between different frames, we give the indexes both in the spatial and temporal axes. In the temporal index file structure, we propose the shot-based B+-tree to index the temporal data. In the spatial index file structure, we use R-tree to store not only the relationships between objects in one frame, but also the relationships of one object when the object first and last appears in the shot. Based on this strategy, we can describe the status of a moving object in details. For the part of query processing, we propose a signature file structure to filter out the videos that absolutely can not be the answer. After that, in order to determine whether the answer exists in the candidate videos, we use a multi-dimensional string, called binary string, to represent the spatial-temporal relationships between objects. Then, the video query processing problem will become a binary string matching problem. Finally, we design and implement an user-friendly user interface. Our system is performed on a Pentium III machine with one CPU clock rate of 550 MHz, 256 MB of main memory, running under Windows 2000 Professional edition, used Access 2000 database and coded in Delphi 6 with about 10,000 lines. From our experience, we show that the proposed system can support an efficient query processing, a fast searching capabilities and an user-friendly user interface.