The quest for control and the subsequent pursuit of continuous quality improvement in the manufacturing sector, due to increasingly keen competition, has stimulated interest in statistical process control (SPC). Whilst traditional SPC techniques are well suited to the mass production industries, their usefulness in short run or low
volume manufacturing environments is questionable. This thesis is primarily concerned with the development of multivariate quality control procedures that can be effectively used in situations where prior estimates of the
process parameters are unavailable. For completeness, some better alternatives to previously proposed procedures are also provided for the case where the process parameters are assumed known in advance of production.