A batch-to-batch optimization methodology for producing a desired molecular-weight distribution (MWD) using an approximate model is presented. The method uses fundamental polymer knowledge in order to simplify an otherwise complicated optimization problem and provide significant freedom in manipulated variable selection. A measurement of the MWD at the end of the batch is used to update manipulated variable trajectories for the next batch, thus iterating into a good operating policy. The optimization approach is then extended for use as an on-line control method. To achieve this, it was necessary to address the modeling of highly correlated measurement error typically observed in the MWD measurements. A multivariable statistical process control (MSPC) monitoring scheme is developed for deciding when a new batch optimization is required. The optimizer remains on, but dormant, while the desired MWD is being produced, and it re-optimizes the process quickly if the process changes and a poor quality polymer is produced. The combined MSPC/batch-to-batch optimizer is demonstrated on a simulated semi-batch polystyrene reactor.