Statistical process control plays a key role in today's highly competitive industrial environment since it allows quality practitioners to timely detect out‐of‐control situations and take actions whenever necessary in order to ensure that the products or services produced correspond to certain quality standards. Control charts are the tools quality practitioners use, and their monitoring performance is of major importance in practical applications. Since the values of the parameters used for the design of the charts' control limits are usually unknown in practice, the practitioners need to estimate them using an in‐control retrospective sample. It has been shown that parameter estimation severely affects the control charts' properties. Many recent studies focused on investigating the impact of parameter estimates on the performance of control charts and on ways of diminishing this impact. This paper aims to provide an up‐to‐date critical review on the methodologies that have recently been developed in this area. Copyright © 2013 John Wiley & Sons, Ltd.
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