Three multivariable filters are evaluated for on-line monitoring of a CSTR polymerization reactor. The first filtering algorithm is the Kalman filter. This linear filter is simple to implementation, but cannot exactly estimate the dynamic behavior of the polymerization reactor. To compensate the state model inadequacies, nonlinear models can be considered in the filtering algorithm. The precise state estimation can be guaranteed by the extended Kalman filter (EKF). Finally, the auto-regressive exogenous inputs model based filter (ARXF) is developed to reduce the modeling cost. These different filters are applied to the continuous solution polymerization of a MMA-AIBN-EA system as a case study. The ARXF is easy to implement and shows satisfactory results.