Control chats are significant part of statistical process control and are in practice not only to produce the quality products but also to maintain the quality standards of industrial production. Control charts got more importance for production of quality products since the inception of ISO-9000 concept. The producers are using control charts not only to produce quality products but also to maintain the best quality of their production. Therefore, researchers are developing various types of control charts applicable for maintaining the quality of different industrial products. However, measurement error affects the efficiency of control charts which is not commonly addressed by the authors. This thesis is an endeavour to develop memory based efficient control charts for industrial production and to study the effect of measurement error on the efficiency of these charts along with possible solution to reduce the error effect to the maximum. Sequence of different chapters is briefly discussed here. First chapter is introduction comprising of brief about quality control, role of control charts to maintain the quality of industrial production, background and simple form of control charts, and different types of control charts. It includes Shewhart’s control charts, EWMA chart, Max-EWMA control chart, and hybrid EWMA control chart. This chapter also includes brief about auxiliary information, its use in control charts and measurement error using covariate error model in order to study the impact of measurement error on the efficiency of control charts. At the end of the chapter, objectives of the study are highlighted. Chapter two provided literature review about control charts for single and joint monitoring, control charts using auxiliary information, control charts with measurement error and hybrid control charts. Proposed EWMA control chart with measurement error for joint monitoring of process mean and variance, is discussed in chapter three. Issue of measurement error through covariate error model and effects of error on the efficiency of EWMA chart are discussed and shown in shape of tables comprising ARLs and SDRLs . In order to reduce the error effect multiple measurements method is also elaborated. For calculations of ARLs and SDRLs a brief algorithm is given. Illustrative example is also given for real life application of proposed control chart. Chapter four is proposed Max-EWMA chart with measurement error using auxiliary variable for joint monitoring. Algorithm for calculations of ARLs and SDRLs is also explained. Efficiency of the proposed chart, effects of measurement error on its efficiency and method of multiple measurements to reduce the error effect are discussed through simulation results. Real life example is also given to study the application of proposed control chart. Chapter five is proposed maximum hybrid EWMA with measurement error control chart. It is proved through simulations that the proposed chart is efficient due to hybrid effect but it is affected by measurement error. In order to reduce the error effect, multiple measurements are tried and found effective to reduce the error impact. Practical example is also included for real life application of our proposed control chart. Maximum hybrid EWMA chart using auxiliary information is proposed in chapter six and maximum hybrid EWMA with measurement error using auxiliary information control chart is proposed in chapter seven. It is proved in chapter six with illustrative example that hybrid chart with auxiliary information is the most sensitive chart for joint monitoring. But it is shown in chapter seven that hybrid EWMA using auxiliary information is affected with measurement error. In order to reduce the error effect, multiple measurements are used in simulation study as well as in real life application. It has been proved in this research work that study of measurement error effect on the efficiency of control chats is utmost necessary otherwise, control charts can lead to misleading results.