Based on Surface Water Eutrophication Control Standard recommended by Ministry of Water Resources of China, a simple neural network ensemble(NNE) model was constructed for comprehensive eutrophication assessment of lake and reservoir. This model adopted Chlorophyll a, Total Phosphorus, Total Nitrogen, Chemical Oxygen Demand and Secchi Depth as inputs, and the output is a continuous variable, which represents the trophic state. 1000 input/output pairs were produced with linear interpolation method according to the above standard. 100 pairs were selected randomly from all data pairs as testing sample, and the rest used as training sample. Back propagation(BP) neural network with same topology structure were applied to all subnets of this ensemble model and were trained using resilient back propagation and gradient descent with momentum as the learning algorithm. The number of hidden nodes of subnet and number of subnets of ensemble are 3 and 40 respectively, determined with the incremental method. All subnets were trained with different initial weights and bias. The results of using this model to assess the trophic state of Chaohu Lake, showed that this model is insensitive to initial weights, and the generalization ability is improved remarkably. With respect to assessment results, there is no apparent difference between this model and the interpolation scoring method, but there is significant difference between this model and the comprehensive trophic level index method. The correlation coefficients of assessment results gained by this model and those by the comprehensive trophic level index method and the interpolation scoring method are 0.9406 and 0.8891, respectively. The results of contrast analysis indicate this model has learned the potential assessment rules from assessment standard, and assessment results of this model are objective and reliable.