Optimisation of controller parameters can be achieved through the use of parameter sensitivity functions in an iterative tuning process. Unlike some other methods for the generation of sensitivity functions, the approach described in this paper avoids the need for explicit a priori knowledge of the plant model structure or parameters, systematic adjustments of controller parameters being made entirely through the processing of signals obtained from tests on the closed-loop system. The methods, based on convolution, may be applied to the estimation of controller parameter sensitivity functions in linear closed-loop systems using simple test inputs, such as step signals. This approach differs from the Iterative Feedback Tuning algorithm in that it does not involve repeated experiments involving system outputs from one test being used as reference inputs in a second test. The signal convolution approach is illustrated through an example involving a simple two-input two-output liquid-level control system involving two coupled tanks.