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In this paper, a bio-inspired pattern recognition system for tin-oxide gas sensor applications is proposed. To mimic the biological olfactory system, temperature modulation is first used to virtually increase the number of sensors by periodically sampling the sensors' response at different temperatures. A convex microhotplate is used in order to improve the thermal properties of the structure enabling efficient temperature modulation process to be carried out. Temperature modulation is shown to increase the number of effective sensors from 16 physically available sensors (integrated on a single chip) to 12 000 virtual sensors (VSs). This enables the emulation of a very large number of sensors typically found in biological systems. The response of each sensor is seen as a fingerprint map, which is further processed using various image processing techniques. Self organized maps (SOMs) algorithm is used to create a 2D map for each gas and to combine the huge number of VSs in order to reduce the dimensionality. Image moments are used as a feature enabling to characterize the spatial distribution within the image lattice and to retrieve the brighter regions in the SOM' nodes exhibiting high activity to the input gas. Experiments on real sensors data show improved performance (96%) as compared with standard gas discrimination algorithms.