Strong consistency results for a class of nonlinear approximate maximum likelihood algorithms for robust system identification are developed, where the system is assumed to be of the ARMAX form. The analysis uses the Martingale results, and strong consistency is shown to hold under a new assumption, representing a generalization of the strictly positive-real condition. Arguments are also given for using Huber’s nonlinearity, in order to reduce the influence of outliers in practice.