The ongoing struggle of information retrieval systems is to present users with information that most relevant user needs. So, IR researchers have begun to expand their efforts to understand the nature of the information need that users express in their queries. If system is able to understand the intension behind user’s needs and contents, it will retrieve more accurate results. This system presents algorithm and techniques for increasing a search service's understanding of user search queries. Web query classification is to classify a web search query into a set of user intended categories. Previous query classification techniques performed classification process on query logs and neighbouring queries in search session time. We propose Query Classification Algorithm (QCA) for automatic topical classification of web queries based on domain specific ontology. Ontology is a specialization of concepts in domain and relationships that holds between those concepts. Using ontology as a controlled vocabulary in the process of classification, performance accuracy is improved in the classification process. Evaluation of classification accuracy and retrieving performance are explored. The system measures the performance accuracy of retrieving documents by using the number of documents relevant with the user intended category by the total number of retrieved documents. Classification accuracy is measured with recall, precision and fmeasure.