A new method for simultaneously selecting the order and identifying the parameters of an ARX model and the control strategy design has been developed. The method is based on the reformulation of the problem in the standard state space form and the subsequent implementation of a bank of minimum variance controllers, each fitting a different order model. Thus, the problem is reduced to selecting the true model among a set of candidate models, using the well-known multi-model partitioning theory, for general (not necessarily Gaussian) data pdf 's. Thus, the cumulative control is the average of the model-conditional minimum variance controls, weighted by the respective a posteriori probability that each particular model is the true model. Simulation experiments indicate that the proposed method is 100% successful in selecting the correct model order and that it accurately identifies the model parameters, in a sufficiently small number of iterations. Furthermore, the method is insensitive in variations of the used filters' variance, and it is adaptive, in the sense that it has the ability of successfully tracking changes in the model structure, in real time. The proposed algorithm lends itself to parallel and VLSI implementations.