Multivariate quality control charts have some advantages for monitoring more than one variable. Nevertheless, there are some disadvantages when multivariate schemes are employed. The main problem is how to interpret the out-of-control signal. For example, in the case of control charts designed to monitor the mean vector, the chart signals show that there is a shift in the vector, but no indication is given about the variables that have shifted. Generalized Variance |S| quality control chart is a very powerful way to detect small shifts in the mean vector. Unfortunately, there are no previous works about the interpretation of the out-of-control signal of this chart. In this paper neural networks are used to interpret the out-of-control signal of the Generalized Variance |S| Chart. The utilization of this neural network in the industry is very easy, thanks to the developed software.