A sliding mode control strategy is proposed for the synthesis of an adaptive learning algorithm in a neuron whose weights are constituted by first-order dynamical filters with adjustable parameters, which in turn allows the representation of dynamical processes in terms of a set of such neurons. The approach is shown to exhibit robustness characteristics and fast convergence properties. A simulation example, dealing with an analog signal tracking task, is provided which illustrates the feasibility of the approach.