Content Based Video Retrieval (CBVR) systems are used for retrieval of desired videos from a large collection on the basis of features extracted from videos. The extracted features are used to index, classify and retrieve desired and relevant videos while filtering out undesired ones. Videos can be represented by their audio, texts, faces and objects in their frames. An individual video possesses unique motion features, color histograms, motion histograms, text features, audio features, features extracted from faces and objects existing in its frames. Videos containing useful information and occupying significant space in the databases are under-utilized unless exist CBVR systems capable of retrieving desired videos by sharply selecting relevant while filtering out undesired videos. Results have shown performance improvement when features suitable to particular types of videos are utilized wisely. Various combinations of these features can also be used to achieve desired performance. Many researchers have an opinion that result is poor when images are used as a query for video retrieval. Here, instead of using a single image or key frames, multiple frames of the video clip being searched are used. Also, instead of using Euclidean Distance to measure similarity Support Vector Machine (SVM) is used. This method used for CBVR system shown in this paper yields an enhanced and higher retrieval results. Also, multiple frames based classification and retrieval yields significantly higher results without the complexity of finding key frames to represent a shot. The system is implemented using MATLAB. Performance of the system is assessed using a database containing 1000 video clips of 20 different categories with each category having 50 clips. The performance is tested using features extracted using Gabor filters as these are most frequently used to represent texture features.