This thesis proposes a new approach in statistical control chart design called Integrated Quality Control (IQC). IQC integrates all components involved in the process of control chart design. In traditional control chart design, chart designers start the process by several given assumptions including the normal distribution of quality variable(s), independence of samples and known model parameters in economical design of control charts. IQC verifies all of these assumptions and provides alternative tools to check if any of these assumptions are violated. To achieve this goal IQC utilizes a rich library of control charts. This thesis further introduces a univariate control chart for nonnormal variables and a cause selecting statistic for identifying the contributing variables in multivariate systems when an out of control signal is detected. In multivariate systems, IQC uses Hotelling T2 and MEWMA control charts for detection of out-ofcontrol signals and by using the introduced cause selecting procedure identifies the outof-control parameters. IQC extends existing research by proposing a method to calculate the cost parameters of the economic model. Economical design of control charts in multivariate system is an important part of control chart design and all of developed models by researchers assume the time between failures in the system follows an exponential distribution. IQC extends the economic design of Hotelling T2 and MEWMA control charts to systems under Weibull shock model which is a more appropriate model for production systems with mechanical parts. Finally, IQC introduces a data streaming protocol that integrates measuring devices with the quality system, overcoming common physical limitations to enable real-time monitoring and control.