The problem of system identification is addressed by means of general neural networks with locally distributed dynamics. These networks are based on both multilayer perceptron and radial basis function structures. Evolutionary algorithms are suggested to select the optimal neural topologies and parameters. The accuracy of the neural models and the complexity of their architectures are evaluated by considering six objective functions organised on a two-level priority hierarchy. The multiobjective optimisation is solved in the Pareto-sense. Special mechanisms are developed, in order to encourage a rapid improvement of the genetic material. Application to a laboratory three-tank system illustrates the approach.