With the rapid increasing popularity of the WWW, Websites are playing a crucial role to convey knowledge and information to the end users. Discovering hidden and meaningful information about Web users usage patterns is critical to determine effective marketing strategies to optimize the Web server usage for accommodating future growth. Most of the currently available Web server analysis tools provide only explicitly and statistical information without real useful knowledge for Web managers. The task of mining useful information becomes more challenging when the Web traffic volume is enormous and keeps on growing. In this paper, we propose a concurrent neurofuzzy model to discover and analyze useful knowledge from the available Web log data. We made use of the cluster information generated by a self organizing map for pattern analysis and a fuzzy inference system to capture the chaotic trend to provide short-term (hourly) and long-term (daily) Web traffic trend predictions. Empirical results clearly demonstrate that the proposed hybrid approach is efficient for mining and predicting Web server traffic and could be extended to other Web environments as well.