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An approach to the design of discrete-time decentralized control systems based on model-based predictive control (MBPC) and neural estimation is proposed. The class of interconnected large-scale systems (LSS) is considered, and a model is used at each control station to predict the corresponding subsystem output over a long time period. ; In the case of subsystems with m-step delay information patterns the non-locally available interaction trajectories are estimated by a multi-layer neural network trained on-line with a modified backpropagation-type algorithm. Representative computer simulation results are provided and compared for a set of illustrative examples. The proposed control scheme shows better performance than the other schemes, and also covers the important case where the subsystems' interactions are nonlinear.