Chemical engineering processes need careful monitoring in order to ensure product specification (i.e., composition, quality, etc.). Physicochemical analytical techniques can be applied for characterization, but those techniques can be time-consuming and become impractical for chemical processes for which online monitoring is needed. Calorimetry is frequently used to monitor and control batch and semibatch reactions. State and covariance estimation for batch processes employing advanced techniques, such as moving horizon estimation (MHE) for state and autocovariance least-squares (ALS) for covariance, have not been studied together. In this paper, the ALS technique is extended for batch processes and validated using laboratory data of three experimental case studies, with focus on polymerization processes. The problem of the simultaneous online estimation of states and parameters in these systems, employing recursive, extended Kalman filter (EKF) and unscented Kalman filter (UKF), and optimization-based (MHE) estimators with covariances estimated by the ALS, is examined. The results of the implementation of the proposed approach show accuracy of the estimated variables for the polymerization processes considered. Thus, this work provides a systematic procedure for the successful determination of covariances and states for experimental batch cases, including systems under repeated perturbations.