In this work, the problem of tuning Kalman filters is addressed. Such tuning usually consists of finding the values of the model and measurements covariance matrices by trial and error methods till satisfactory results have been obtained. Here, we propose a new method to endow the model covariance matrix with physical meaning, enabling a more systematic gain tuning procedure. Simulation examples for a simulated non-isothermal continuous reactor and for an experimental reactor where carbon monoxide oxidation takes place are presented.