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A neural-network-based technique is described to determine the depth profile of a fatigue crack in a metal from the output signal of an alternating current field measurement (ACFM) probe. The main feature of this technique is that it requires only the measurements along the crack opening. The network uses the multilayer perceptron structure for which the training database is established by systematically producing semi-elliptical multi-hump cracks with narrow openings and random lengths and depth profiles. A fast pseudo-analytic ACFM probe output simulator is also used to produce network input data around each crack for a specified inducer. To demonstrate the accuracy of the proposed inversion technique, the simulated results of cracks with both common and complex geometries are studied. The comparison of the actual and reconstructed depth profiles substantiates the technique introduced here. To further validate the technique, the experimental results associated with several fatigue cracks of complex geometries are presented.