We propose a model for characterizing amplitude and phase probability distributions of eddy‐current signals. The squared amplitudes and phases of the potential defect signals are modeled as independent, identically distributed (i.i.d.) random variables following gamma and von Mises distributions, respectively. We derive a maximum likelihood (ML) method for estimating the amplitude and phase distribution parameters from measurements corrupted by additive complex white Gaussian noise. Newton‐Raphson iteration is utilized to compute the ML estimates of the unknown parameters. The obtained estimates can be used for flaw detection as well as efficient feature extractors in a defect classification scheme. Finally, we apply the proposed method to analyze rotating‐probe eddy‐current data from tube inspection of a steam generator in a nuclear power plant.