A timely detection of assignable causes plays a significant role in the performance of anyprocess.Control charts are effective process monitoring tools which help differentiatingbetween natural and assignable causes of variations.It is always desirable to have an efficient design structure of control charts for an improved monitoring of process parameters.This thesis contributes some improved control charting structures to be used as add-in forStatistical Process Control (SPC) toolkit.The proposed charting structures are designedfor location and scale parameters using the information on some auxiliary characteristics.The performance ability of the proposals is evaluated in terms of some useful measuresincluding average run length (ARL), extra quadratic loss (EQL), average time to signal(ATS), average extra quadratic loss (AEQL), Relative ARL (RARL) and Run Length (RL)properties such as: Median Run Length (MDRL), Standard Deviation of Run Length distribution (SDRL).These performance measures are examined for normal, gamma and tdistributed process environments (with and without contaminations) using simple randomand double sampling schemes.We have investigated and compare the performance of different proposed charting structures using extensive Monte Carlo simulations.We have alsoincluded some real situations in order to highlight the practical application of the proposalscovered in this study.