Process monitoring and control requires estimation of quality variables, which are often not measurable directly. A cost effective approach to monitor these variables in real time is to employ model based soft sensing and state estimation techniques. Dynamic model based state estimation is a rich and highly active area of research and many novel approaches have emerged over last few years. In this paper, we review recent developments in the area of recursive nonlinear Bayesian state and parameter estimation techniques. © 2011 Zhejiang University.