A content-based scene indexing has been important technique for an effective video contents handling such as scene retrieval and editing. The standard multimedia content descriptor (MPEG7) has been proposed for the key scene indexing. As for an automatic scene indexing, audio-visual features are most important clues. Many methods have been proposed for effective scene indexing based on those features. In this paper, we propose an automatic key scene detection method for baseball video contents using video features. We regard pitching scenes as key scenes, because they are starting points of all baseball play scenes. If the pitching scenes are detected, they could be effective hints to detect other scenes. In addition, a pitching scene digest video can be easily edited by gathering automatically extracted scenes. The pitching scene digest can be useful data for pitching analysis. We extract pitching scenes using color, domain and motion template created from manually selected pitching scene samples. Those templates contain image features unique to pitching scenes. Template matching is applied to video stream, so that target scenes can be detected by judging calculated matching rate. We experimentally test our method for actual baseball video contents. It can be useful data for pitching analysis and editing of digest news broad casting. We are developing the video indexing support system which users can give text annotations to indexed scenes using MPEG7 format descriptors.