The problem of unsolicited email has continued to increase every month for years. In order to deal with the huge amount of spam received day by day, we combined multiagent systems in a peer-to-peer framework with text categorization to identify spam. The content of the emails is analyzed by the classification algorithm “Support Vector Machines”. Information about spam is exchanged between the agents through the networks identification numbers for emails, which where identified as spam, are generated and forwarded to all other agents, connected to the network. These numbers allow agents to identify incoming spam email. Our paper shows that, by this way, powerful email filters with high reliability based on distributed design can be achieved.