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This paper presents a new neural network-based nonlinear adaptive model predictive control algorithm and its implementation over a service-oriented computer network. The computer network is based on the device profile for web services. At each sampling instant, the algorithm identifies a nonlinear process model using a recurrent neural network. On the basis of the identified model, the nonlinear adaptive model predictive control is updated and the control actions are applied. The network training is performed with data obtained from the prior plant operation under different input disturbances. The proposed nonlinear model identification and nonlinear adaptive model predictive control were applied to the temperature control of a fluidized bed furnace reactor and tested by simulating the reactor operation on the proposed service-oriented computer network. Input step changes and long range output prediction results show good predictive and adaptive control performance. The computation time of the proposed algorithms running on the proposed network architecture was less than the sampling period of the process with a bounded round trip delay. These simulation results indicate that the proposed identification and control algorithms can be of practical use to processes with similar dynamics with the fluidized bed reactor. This is because their realization over a service-oriented computer network which may be the physical platform of their implementation does not introduce delays of such a level that may alter the required sampling time for good control performance.