This paper describes a methodology for the simulation of out-of-control situations using in-control data, for the purpose of identifying the potential alarms that could occur in a multivariate process. The method is based on finding the independent factors of the variability of the process and shifting these factors one by one. The effects of these shifts are then translated in terms of the observed variables. The shifts provoked by the most important factors are called principal alarms. The principal alarms can be plotted, visualizing the main deviations of the process. Also, a resampling procedure for the estimation of the average run length of a control chart using principal alarms is proposed. An application with a real industrial process illustrates the usefulness of the methodology.