In some real situations there is the need of controlling p variables of a multivariate process, where p
1 out of these p variables are easy and inexpensive to monitor, while the p
2=p–p
1 remaining variables are difficult and/or expensive to measure. However, this set of p
2 variables is important to quickly detect the process shifts. This paper develops a control chart based on the T
2 statistic where normally only the set of p
1 variables is monitored, and only when the T
2 value falls in a warning area the rest of variables (p
2) are measured and combined with the sample values from the p
1 variables, in order to obtain a new T
2 statistic. This new chart is the double dimension T
2 (DDT2) control chart. The ARL of the DDT2 chart is obtained and the chart's parameters are optimized using genetic algorithms with the aim of maximizing the performance in detecting a given process shift. The optimized DDT2 chart is compared against the standard T
2 chart when all the variables are monitored. The results show that the DDT2 clearly outperforms T
2 chart in terms of cost, and in some cases even detects process shifts faster than the latter. In addition, friendly software has been developed with the objective of promoting the real application of this new control chart.
