There are many studies that have been conducted about the integrated use of statistical process control (SPC) and engineering process control (EPC) because using them individually cannot optimally control the manufacturing process. The majority of these studies have reported that the integrated approach has better performance than using only SPC or EPC. Among all these studies, most of them have assumed that the assignable causes of process disturbance can be effectively identified and removed by SPC techniques. However, these techniques are typically time-consuming and thus make the search hard to implement in practice. The paper discusses the development of neural network models with independent component analysis (ICA) to identify the disturbance and recognize shifts in the correlated process parameters. Moreover, these designed network models can be used to monitor and eliminate manufacturing process parameters when disturbance happens in the underlying process. For comparison, the traditional Shewhart chart and cumulative sum (CUSUM) chart, were constructed for the simulated data sets to evaluate the identifying capability of the proposed approach. As the results reveal, the proposed approach outperforms the other methods and the shift of disturbance can be identified successfully.