Stationary process gains are critical model parameters for determining targets in commercial MPC technologies. Consequently, important savings can be reached by accessing an early prevention method capable of detecting whether the actual process moves away from the modeled dynamics, particularly by indicating when the process gains are no longer represented by those included in the model identified during commissioning stages. In this first approach, a subspace identification method is used under open-loop process condition to estimate the process gain matrix. The main reason for using the subspace identification (SID) method is that it works directly with raw data; it directly yields a multivariable state space model and has proved to be successful in dealing with multivariable processes and periodic batch-wise data collection. To detect significant changes in the estimator population, a monitoring sequence of hypothesis tests can be done through simple confidence limits directly on each gain estimator, or increasing the sensitivity by using the exponentially weighted moving average (EWMA) or the cumulative sum (CUSUM) algorithms. The objective of this aticle is to present a rational combination of inferential tools capable of detecting which gain of a multivariable model starts moving away from its original value. The anticipated knowledge of these events could provide a warning to process engineers and prevent targeting process conditions with wrong gain estimations. The regular follow-up of the gain matrix should also help to localize those dynamics needing an updating identification and reduce the frequency of time-consuming re-identification of the complete model.