Process capability analysis and have been widely used in the manufacturing industry for measuring process reproduction capability according to its manufacturing specifications. One of the methods for process capability analysis is indices. Process capability indices have been widely used in the manufacturing industry for measuring process reproduction capability according to manufacturing specifications. Properties of the univariate processes have been investigated extensively, but are comparatively neglected for multivariate processes where multiple dependent characteristics are involved in quality measurement. In real applications, manufactured products often have multiple quality characteristics and multiple characteristics processes are by now so common that our studies to capability indices can't be restricted to the univariate domain. That is, the process capability analysis involves more than one engineering specification. For this reason, multivariate methods for assessing process capability are proposed. Since the quality of data on the process characteristics relies very much on the gauge measurement accuracy, therefore the capability indices of the process by ignoring the measurement errors would not be reliable. So the gauge measurement capability has an important effect on determining the process capability and this effect grows with increasing the gauge measurement error. Any variation in the measurement process has a direct impact on the ability to make sound judgment about the manufacturing process. Conclusions about capability of the process without considering the gauge measurement capability are not reliable and in general, producers will be damaged if they ignore this fact. We also claim that by considering the gauge measurement capability for estimating the process capability, we get a better estimation. Consequently, the more real estimations of process capability help the quality experts to adopt a better condition to improve the quality of the production. In this paper, we consider the effect of Gauge Measurement capability on the multivariate process capability index (MC p ). Also with respect to measurement capability; we investigate the statistical properties of the estimated MC p and consider the effect of Gauge Measurement capability on the lower confidence bound, hypothesis testing, critical value and power of testing for MC p at the mentioned state. The results of analysis show that Gauge Measurement capability wills notability change the results of estimating and testing of process capability. With present adjusted confidence interval bounds and critical values for capability testing purpose of MC p with unavoidable measurement errors, we have suitable estimation and test. Our research would help quality experts to determine whether the factory processes meet the capability requirement, and make more reliable decisions.