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The main goal in many processes is to obtain consistent and reproducible operation and end-quality properties. In this thesis the problem of product quality control in batch and semi-batch processes is addressed. Unlike from much of the published literature that uses first principles models, this thesis studies the end-quality feedback control problem using only empirical Partial Least Squares (PLS) models. Several simple, practical and effective regulatory control strategies are proposed. The thesis consist of four main chapters: i) On-line control of a distributed end quality property (particle size distribution, PSD) using mid-course correction strategies (MCC), ii) an inferential-adaptive control approach that combines on-line and batch-to-batch control, iii) a novel reduced dimensional space control algorithm to obtain complete manipulated variable trajectories (MVT) consistent with past operation, and iv) incorporation of prior batch-to-batch information for batch analysis and monitoring. In the first section, three on-line empirical MCC strategies are proposed for the control of bimodal PSDs in emulsion polymerization systems. The performance of the control strategies is evaluated using a detailed theoretical simulator. Control is applied only when the predicted properties falls outside a statistically defined "no-control" region. Each control strategy corresponds to a control objective: i) Control of second mode of the distribution, ii) control of the full bimodal PSDs and iii) control of relative distributions. Advantages and disadvantages of each one of the control strategies are discussed. In the second part a combined on-line and batch-to-batch control strategy is presented. The approach extends MCC strategies used before to include multiple decision and correction points, batch-to-batch information to reject batch-wise correlated disturbances, and an adaptive PLS approach to update the models from batch-to-batch to overcome model, Doctor of Philosophy (PhD)