We report on our research efforts towards developing efficient equipment for the automatic recognition of insects using only the acoustic modality. Specifically, we deal with three groups of insects, namely the crickets, cicadas and katydids. Inspired by well-documented tactics of speech processing, the signal processing employed in the present work is elaborated further with respect to the sound production mechanisms of insects. In order to improve the practical efficacy of our equipment, we adopt a score-level fusion of classifiers with non-parametric (probabilistic neural network) and parametric (Gaussian mixture models) estimation of the probability density function. An efficient hierarchic classification scheme is introduced, where the identification of unlabelled input takes place at various levels of hierarchy, such as suborder, family, subfamily, genus and species. We evaluate the practical significance of our approach on a large and well-documented catalogue of recordings of crickets, cicadas and katydids. For the hierarchic classification scheme, we report identification accuracy that exceeds 99% at suborder and family levels. In the straight classification scheme, we report accuracy of 90% for 307 species.