In this paper, we present a novel statistical approach, called the weighted voting method, for automatic news video story categorization based on the closed captioned text. News video is initially segmented into stories using the demarcations in the closed captioned text, then a set of keywords is extracted to form a feature vector for further processing. The categorization is achieved by computing the likelihood score for each category and the knowledge base is updated incrementally in linear time. We have used the proposed method to categorize 425 news stories from CNN and compared the categorizing performance with SNoW and Bayes decision method. For the varying size of training examples, our approach achieved the highest categorization accuracy among the three approaches.