This paper examines the use of a so-called ``generalised Hammerstein--Wiener'' model structure that is formed as the concatenation of an arbitrary number of Hammerstein systems. The latter are taken here to be memoryless non-linearities followed by linear time invariant dynamics. Hammerstein, Wiener, Hammerstein--Wiener and Wiener- -Hammerstein models are all special cases of this structure. The parameter estimation of this model is investigated by using a standard prediction error criterion coupled with a robust gradient based search algorithm. This approach is profiled using a Wiener-- Hammerstein benchmark example, which illustrates it to be effective and, via Monte--Carlo simulation, relatively robust against capture in local minima.