Plant data bases are filled with information on the relationships of state (output) and input variables. However, due to natural high multi-collinearities of the inputs and low signal to noise ratios for the outputs, modelers are challenged in developing cause and effect relationships using plant data. As a result, dynamic models are commonly developed from plant tests which incur costs and risks to the operation. Thus, the purpose of this work is to introduce a modeling approach that is capable of developing accurate cause and effect models from plant data. The proposed method is a special application of the Wiener block-oriented system and the unique and powerful attributes of this approach over existing techniques are demonstrated in a mathematically simulated process study.