Current centralized trust models are inappropriate to apply in a large distributed multi-agent system, due to various evaluation models and partial observations in local level reputation management. This paper proposes a distributed reputation management structure, and develops a global reputation model. The global reputation model is a novel application of neural network techniques in distributed reputation evaluations. The experimental results showed that the model has robust performance under various estimation accuracy requirements. More important, the model is adaptive to changes in distributed system structures and in local reputation evaluations.