Content-based retrieval allows finding information by searching its content rather than its attributes. The challenge facing content-based video retrieval (CBVR) is to design systems that can accurately and automatically process large amounts of heterogeneous videos. Moreover, content-based video retrieval system requires in its first stage to segment the video stream into separate shots. Afterwards features are extracted for video shots representation. And finally, choose a similarity/distance metric and an algorithm that is efficient enough to retrieve query – related videos results. There are two main issues in this process; the first is how to determine the best way for video segmentation and key frame selection. The second is the features used for video representation. Various features can be extracted for this sake including either low or high level features. A key issue is how to bridge the gap between low and high level features. This paper proposes a system for a content based video retrieval system that tries to address the aforementioned issues by using adaptive threshold for video segmentation and key frame selection as well as using both low level features together with high level semantic object annotation for video representation. Experimental results show that the use of multi features increases both precision and recall rates by about 13% to 19 % than traditional system that uses only color feature for video retrieval