Quick and correct detection and identification of process faults are extremely important when efficient, economic and safe operation of chemical processes is concerned. Undetected process fault may lead to poor quality off-spec products, resulting in poor plant economy and sometimes even catastrophic consequences like accidents, injury to plant personnel. Successful detection and identification of process faults at an early stage can increase the success rate of fault recovery during operations and prevent costly accidents, unnecessary shutdowns. Detection and diagnosis of process faults in chemical processes has been an active area of research. In the literature, several methodologies have been proposed for fault detection and identification (FDI) in chemical processes (Venkatasubramanian et al. 2003 a,b,c; Dash et al. 2000, Chiang et al. 2001) including principal components analysis (PCA), artificial neural-networks (ANN), self-organizing maps (SOM), qualitative trend analysis (QTA), signal processing methods or first principles models. Each of these methods has its advantage and weakness in practical application. To overcome the limitations of an individual method, one needs to develop a system in which multiple FDI methods are judiciously combined (fused) or new fault detection and identification (FDI) systems.