The iterative data-driven method labelled Correlation-based Tuning (CbT) is considered in this paper for the tuning of linear time-invariant multivariable controllers. The approach allows one to tune some elements of the controller transfer function matrix to satisfy the desired closed-loop performance, while the other elements are tuned to mutually decouple the closed-loop outputs. Using CbT, perfect decoupling can be achieved by decorrelating a given reference with the non-corresponding outputs. The controller parameters are calculated either by solving a correlation equation (decorrelation procedure) or by minimizing a cross-correlation function (correlation reduction). The two approaches are compared via a simple numerical example. In addition, the correlation-reduction approach is applied to the simulation model of a gas turbine engine and compared to standard Iterative Feedback Tuning for MIMO systems.