the never-ending growth of Web services and Web-based information systems, the volumes of click stream and user data collected by Web-based organizations in their daily operations has reached enormous proportions. Analyzing such huge data can help to evaluate the effectiveness of promotional campaigns, optimize the functionality of Web-based applications, and provide more personalized content to visitors. In the previous work, we had proposed a method, Grey Relational Pattern Analysis using Markov chains, which involves to discovering the meaningful patterns and relationships from a large collection of data, often stored in Web and applications server access logs, proxy logs etc. Herein, we propose a novel approach to analyse the navigational behavior of User using GRPA with Variable-Length Markov Chains. A VLMC is a model extension that allows variable length history to be captured. GRPA with Variable- Length Markov Chains, which reflects on sequential information in Web usage data effectively and efficiently, and it can be extended to allow integration with a Web user navigation behavior prediction model for better Web Usage mining Applications.