Journal of Applied Sciences1812-56541812-5662Asian Network for Scientific Information10.3923/jas.2010.2516.2522LemmaD.T.RamasamyM.ShuhaimiM.1220101021The widely used dynamic models for identification of linear time invariant systems in process industries are Auto Regressive with Exogenous Input (ARX) and Finite Impulse Response (FIR) models. Their popularity is due to their simplicity in developing the model. However, they need very large amount of data to reduce variance error, in addition ordinary ARX model structures lead to inconsistent model parameters. Orthonormal Basis Filter (OBF) model structures permit incorporation of prior knowledge of the system in the form of one or more poles, which renders it the capacity to capture the system dynamics with a few number of parameters (parsimonious in parameters). In addition, the resulting OBF models are consistent in parameters. The model parameters can be easily developed using linear least square method. In this study, OBF model development for simulation and real case studies is presented.]]>Heuberger, P.S.C., P.M.J. Van de Hof and O.H. Bosgra,1995Tufa, L.D., M. Ramasamy, S.C. Patwardhan and M. Shuhaimi,2008Ljung, L.,1999Nelles, O.,2001Ninness, B.M. and F. Gustafsson,1997Patwardhan, S.C. and S.L. Shah,2005Patwardhan, S.C. S. Manuja, S. Narasimhan and S.L. Shah,2006Van den Hof, P., B. Walhberg, P. Heurberger, B. Ninness, J. Bokor and Oliver T. Silva,2000Van den Hof, P.M.J., P.S.C. Heuberger and B. Wahlberg,2005Wahlberg, B.,1991