The Smooth Variable Structure Filter (SVSF) is a newly-developed predictor-corrector filter for state and parameter estimation . The SVSF is based on the Sliding Mode Control concept. It defines a hyperplane in terms of the state trajectory and then applies a discontinuous corrective action that forces the estimate to go back and forth across that hyperplane. The SVSF is suitable for fault detection and identification applications because of its stability and robustness in modeling uncertainties. The SVSF has two indicators of performance; the a posteriori output error and the chattering. The latter as a signal-contains the system's information which is proven and explored in this paper. The SVSF is applied for the identification of pneumatic systems in order to verify the proposed method. Furthermore, the proposed method is compared with neural network and the results reveal that SVSF is better in identifying nonlinear systems.