Use of a pseudo random binary sequence as an input signal for identification of finite impulse response models of chemical process systems has been quite popular. In this paper, we show the utility of another binary signal, the inverse repeat sequence (IBS) signal in identification of chemical processes. The attractiveness of IRS lies in the fact that, by its design, even-order kernels in the process response are canceled out. This reduces the effect of nonlinearity in identification. The improvements that can be derived by using IRS are shown through the aid of continuous stirred tank reactor and fluidized catalytic cracking unit; case studies. Fm ther, the utility of the identified models in the model predictive control framework is also demonstrated.