This paper introduces a novel feature extraction technique for the analysis of spectral line Stokes profiles. The procedure is based on the use of an autoassociative artificial neural network containing nonlinear hidden layers. The neural network extracts a small subset of parameters from the profiles (features), from which it is then able to reconstruct the original profile. This new approach is compared to two other procedures that have been proposed in previous works, namely principal component analysis and Hermitian function expansions. Depending on the target application, each of these three techniques has some advantages and disadvantages, which are discussed here.