Nowadays, in industry, there are many situations in which the simultaneous monitoring or control of two or more related quality–process characteristics is necessary. Monitoring these quality characteristics independently can be very misleading. Process monitoring of problems in which several related variables are of interest are collectively known as multivariate statistical process control. The most useful tool of multivariate statistical process control is the quality control chart. Multivariate process control techniques were established by Hotelling in his 1947 pioneering paper . Statistical process control (SPC) is an approach that uses statistical techniques to monitor. Although many different multivariate control procedures exist, it is belief that a control procedure built on T 2 statistic possesses all the characteristics that is necessary. Like many multivariate charting statistics, the T 2 is a univariate statistic. This is true regardless the number of process variables used in computing it. However, because of its similarity to a univariate Shewhart chart, the T 2 control chart is sometimes referred to as a multivariate Shewhart chart. This relationship to common univariate charting procedures facilitates the understanding of this charting method. Signal interpretation requires a procedure for isolating the contribution of each variable and/or a particular group of variables. As with univariate control, out-of-control situations can be attributed to individual variables being outside their allowable operational range. The signal interpretation procedure covered in this text is capable of separating a T 2 value into independent components. One type of component determines the contribution of the individual variables to a signaling observation, while the other components check the relationships among groups of variables. This procedure is global in nature and not isolated to a particular data set or type of industry. The T 2 statistics is one of the more flexible multivariate statistics. It gives excellent performance when used to monitor independent observation from a steady state continuous process. It also can be based on either a single observation or the mean of a subgroup of n observation. Many industrial processes produce observations containing time dependency. The T 2 statistic can be readily adapted to these situations and can be used to produce a time-adjusted statistic. For many problems, control limits could not be so precise. Uncertainty comes from the measurement system including operators and gauges, and environmental conditions. In this context, fuzzy set theory is a useful tool to handle this uncertainty. Numeric control limits can be transformed to fuzzy control limits by using membership functions. If a sample mean is too close to the control limits and the used measurement system is not so sensitive, the decision may be faulty. Fuzzy control limits provide a more accurate and flexible evaluation