The traditional C-chart by Shewhart has been widely applied for monitoring count data in industrial and nonindustrial processes. However, using C-chart always experiences an excessive amount of false alarms, since control limits of traditional C-chart are defined by impractical normal assumption. Specially, when we monitor two or more correlated characteristics of defects, C-chart becomes unsuitable. Thus, monitoring a process by traditional C-chart leads to the increase of unnecessary costs of inspection.  There are many works that have attempted to improve C-charts. In this thesis, 11 selected improved versions of C-chart are presented. The performances of improved C-charts are evaluated in term of numerical results to demonstrate the sensitivity of the charts and costs of inspections.  We also propose an optimal bivariate Poisson field chart to monitor two correlated characteristics of defects. Our chart is based on the optimization of bivariate Poisson confidence interval and projection of bivariate Poisson data in Poisson field. The detailed description of our proposed algorithm is presented by numerical data. The experimental results demonstrate improved performances regarding user-friendly visualization and false alarm rate  Furthermore, we propose an optimal diagonal inflated bivariate Poisson field chart to monitor two over/under dispersed correlated count data. The detailed description of our chart will be presented. The experimental results demonstrate improved performances according to loss function and false alarm rate compared to other methods.