In this paper, we propose a new hybrid recommendation model for web users which is based on multiple recommender systems working in parallel. With the rapid growth of the World Wide Web (www), it becomes a critical issue to find useful information from the Internet. Web recommender systems help people make decisions in this complex information space where the volume of information available to them is huge. Recently, a number of approaches have been developed to extract the user behavior from her navigational path and predict her next request as she visits Web pages. Some of these approaches are based on nonsequential models such as association rules and clustering, and some are based on sequential patterns. In this paper, we present a hybrid recommender model which combines the results of multiple recommender systems in an effective way. We have conducted a detailed evaluation on four different web usage data. Our results show that combining recommendation algorithms effectively leads a better recommendation accuracy. The experimental evaluation shows that our method can achieve a better prediction accuracy compared to standard recommendation systems while still guaranteeing competitive time requirements.