In some statistical process control applications, the quality of a product or process can be characterized by a linear or non-linear regression relationship between a response variable and one or more explanatory variables, which is referred to as "profile". The response variable represents the quality of product or process and explanatory variables indicate the factors affecting quality. Studying and controlling the quality of the product or process will be done by monitoring the profile and using control charts that are known as a powerful tool for monitoring profiles over time. Basically, any standardized method in statistical process control, takes place in two phases I and II, which have different design goals. In most studies in profile monitoring in phase II, it is assumed that the process parameters are known. However, in many applications, this condition is not met, and the parameters are estimated using the in control data set collected in phase I. The present study investigates and compares some methods for monitoring phase II of the second order polynomial profiles as under conditions where the parameters of this profile include ,, and standard deviation (?) are estimated using phase I data and the method that have a better performance, compared to other methods in this situation, will be highlighted. Also the effect of estimates derived from two widely used methods for estimating parameters in phase I, including F and T 2 methods, will be studied on the performance of control charts in Phase II and the method that will have less effect on the performance of control charts will be determined. Phase II monitoring methods are Orthogonal, MEWMA and dEWMA-OR that are evaluated and compared using ARL, SDRL, AARL and SDARL metrics. The results show that, if the process in phase II is controlled and the estimated parameters are used, the MEWMA method in terms of the ARL metric and the Orthogonal method in terms of the SDARL metric will perform better than other comparable methods. If the process is out-of-control, in terms of the ARL, dEWMA-OR method in detecting small shifts in parameters and the Orthogonal method in detecting moderate and large shifts will have better performance and discover the shifts faster than other methods. By comparing the two estimation methods used in this study, the results show that in both in control and out of control conditions, the use of the F method to estimate process parameters leads to a better performance of control charts in phase II.