This paper addresses an integrated information mining techniques for broadcasting TV-news. The utilizes technique from the fields of acoustic, image, and video analysis, for information on news title, reporters and news background. The goal is to construct a compact yet meaningful abstraction of broadcast TV news, allowing users to browse through large amounts of data in a non-linear fashion with flexibility and efficiency. By using acoustic analysis, a news program can be partitioned into news and commercial clips, with 90% accuracy on a data set of 400 hours TV-news recorded off the air from July 2005 to August of 2006. By applying additional speaker identification and/or image detection techniques, each news stories can be segmented with a better accuracy of 95.92%. On screen captions and screen characters are recognized by video OCR techniques to produce the title of each news stories. Then keywords can be extracted from title to link related news contents on the WWW. In cooperation with facial and scene analysis and recognition techniques, OCR results can provide users with multimodal query on specific news stories.