In our Project, A trust model for P2P networks is presented, in which a peer can develop a trust network based on quality of service. The quality of service is evaluated based on two contexts.  The two contexts are trust, service and recommendation contexts. It is defined based on capabilities of peers in providing services and giving recommendations. Interactions and recommendations are considered with satisfaction, weight, and fading effect parameters. A recommendation contains the recommender’s own experience, information from its acquaintances, and level of confidence in the recommendation. These parameters provided us a better assessment of trustworthiness.  This paper presents distributed algorithms that enable a peer to reason about trustworthiness of other peers based on past interactions and recommendations. Peers create their own trust network in their proximity by using local information available and do not try to learn global trust information. Interactions and recommendations are evaluated based on importance, recentness, and peer satisfaction parameters. Finally, it chooses the good peers were able to form trust relationships in their proximity and isolate malicious peers.