The identification task consists of making a model of a system from measured input and output signals. Wiener models consist of a linear dynamic system, followed by a static nonlinearity. We derive an algorithm to calculate the maximum likelihood estimate of the model for this class of systems. We describe an implementation in some detail and show simulation results where a test system is successfully identified from data.