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We investigate the use of statistical learning techniques for fish age estimation from otolith images. The core of this study lies in the definition of relevant image-related features. We rely on the characterization of a 1D signal summing up the image content within a predefined area of interest. Fish age estimation is then viewed as a multi-class classification issue using neural networks and SVMs. A procedure based on demodulation and remodulation of fish growth patterns is used to improve the generalization properties of the trained classifiers. We also investigate the combination of additional biological and shape features to the image-related ones. The performances are evaluated for a database of several hundred of plaice otoliths.