A web is a repository of millions of data. As years progress, tremendous improvement in technology leads to further increase in the amount of data present in the web. Query Classification is an important as well as a difficult problem in the field of information retrieval. Once the category to which query has to be mapped is known, a search engine can return more representative web pages to the users. A new approach to obtain the most prominent target categories for a query using intermediate categories from the directory knowledge and Normalized Web Distance (NWD) is proposed. Semantic relatedness using NWD, frequency and position of the intermediate categories are the factors used to determine the target categories of the given query. It was found that this approach when compared with manually classified dataset yields good results in terms of precision and recall

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