We propose a sequential M-estimation algorithm as an alternative to sequential least squares. Being an approximation of the exact M-estimator, the proposed technique is robust to non-Gaussian processes and outperforms sequential least squares. Simulation results demonstrate the power of the proposed sequential M-estimator.