1. A method of developing multiple models adapted for various stages of a non-linear process control comprising: (a) clustering dynamic data of a data set so as to permit multiple local data regions to be identified as a function of substantial similarity between the data within the local data regions, wherein the data in the data set represents dynamic behavior of the non-linear process; (b) developing a discrete model corresponding to each of the local data regions; (c) reducing the number of the discrete models based on a prediction error between actual outputs and predicted outputs provided by the discrete models in response to corresponding inputs and based on a stability similarity between pairs of discrete models thereby producing a reduced model set; and, (d) selecting at least some of the models in the reduced model set for deployment.