In this paper I propose a method that, given a query submitted to a search engine, suggests a list of related queries. Query recommendation is a method to improve search results in web. This paper presents a method for mining search engine query logs to obtain fast query recommendation on a large scale. Search engines generally return long list of ranked pages, finding the important information related to a particular topic is becoming increasingly difficult and therefore, optimized search engines become one of the most popular solution available. In this work, an algorithm has been applied to recommend related queries to a query submitted by user. For this, the technology used for allowing query recommendations is query log which contains attributes like query name, clicked URL, rank, time. Then, the similarity based on keywords as well as clicked URL’s is calculated. Additionally, clusters have been obtained by combining the similarities of both keywords and clicked URL’s. The related queries are based in previously issued queries The method not only discovers the related queries, but also ranks them according to a relevance criterion. In this paper the rank is updated only the clicked URL, not all the related URL’s of the page.