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Iterative feedback tuning is a direct tuning method using closed‐loop experimental data. The method is based on numerical optimization and in each iteration an unbiased gradient estimate is used. Due to these unbiased gradient estimates, the method converges to a stationary point of the control criterion provided the closed loop signals remain bounded throughout the iterations. In this contribution, it is shown how such unbiased estimates can be obtained for multivariable linear time‐invariant systems. Particular attention is given to the issue of keeping the experiment time to a minimum and several efficient algorithms are presented. It is shown that, for tuning an arbitrary linear time‐invariant multivariable controller with n_w inputs and n_u outputs, 1+n_u×n_w experiments are sufficient in each iteration of the algorithm. For disturbance rejection, an alternative algorithm is proposed which requires n_u+n_w experiments …