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Automatic classification of seismic signals has been typically carried out on feature-based representations. Recent research works have shown that constructing classifiers on dissimilarity representations is a more practical and, sometimes, a more accurate solution for some pattern recognition problems. In this paper, we consider Bayesian classifiers constructed on dissimilarity representations. We show that such classifiers are a feasible and reliable alternative for automatic classification of seismic signals. Our experiments were conducted on a dataset containing seismic signals recorded by two selected stations of the monitoring network at Nevado del Ruiz Volcano. Dissimilarity representations were constructed by calculating pairwise Euclidean distances and a non-Euclidean measure on the normalized spectra, which is based on the difference in area between spectral curves. Results show that even though Euclidean dissimilarities have advantageous properties, non-Euclidean measures can be beneficial for matching spectra of seismic signals. [ABSTRACT FROM AUTHOR]