A method includes obtaining historical data associated with an industrial process, which is associated with multiple independent variables. The method also includes automatically excluding at least one portion of the historical data and automatically extracting data segments from at least one non-excluded portion of the historical data.

The method further includes iteratively performing model identification using the data segments to identify one or more models and using the model(s) to design, monitor, or tune at least one industrial process controller for the industrial process. Iteratively performing the model identification includes recursively analyzing the data segments to (i) select the data segment(s) associated with each variable that have a highest energy and provide a high signal to noise ratio and (ii) eliminate poorly performing segments associated with each variable. Iteratively performing the model identification also includes generating a model for each variable using the selected data segment(s) for that variable.