A novel real-time final product quality control strategy for batch operations is presented. Quality control is achieved by periodically predicting the final product quality and adjusting process variables at pre-specified decision points. This data-driven methodology employs multiple models, one for each decision point, to capture the time-varying relationships. These models combine real-time batch information from process variables and initial conditions with information from prior batches. Design of experiments is performed to generate informative data that reveal the relationship between process conditions and the final product quality at various times. Control action is also taken at pre-specified decision points; at these times, the manipulated variable values are calculated by solving an optimal control problem similar to model predictive control. A key benefit of this strategy is that missing data imputation is obviated. The proposed modeling and quality control strategy is illustrated using a batch reaction case study. (C) 2008 Elsevier Ltd. All rights reserved.