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This work proposes a new neural approach based on the structure of a Multi-Layer Perceptron (MLP) for identify- ing current harmonics in power systems. The learning approach is based on several MLP, adopts the Fourier decomposition of a signal and a training set generated from harmonic waveforms is used to calculate the weights. After training, each MLP is able to identify two coefficients for each harmonic term of the input signal. The effectiveness of the new approach is evaluated by experiments. Results show that the proposed MLPs of the new approach enable to identify effectively the amplitudes of harmonic terms from the signals under noisy condition. Results are compared to other and recent MLP approaches. The new approach can be applied in harmonic compensation strategies by being implement in an active power filter to ensure the power quality in electrical power systems. [ABSTRACT FROM AUTHOR]