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Landmines are a significant barrier to financial, economic and social development in various parts of the world. The demand for dependable, trustworthy intelligent diagnostic systems in the field of landmine detection has been increasing rapidly. Metal detectors used in mine-decontamination, cannot differentiate a mine from metallic debris where the soil contains large quantities of metal scraps and cartridge cases, so a device is required that will reliably confirm that the ground being tested does not contain an explosive device, with almost perfect reliability. Human experts are unable to give belief and plausibility to the rules devised from the huge databases. In this paper a hybrid classifier has been developed that uses rough sets theory and neural networks architecture to classify mines from the non-mines with better results. Rough sets have been applied to classify the landmine data because in this theory no prior knowledge of rules are needed, these rules are automatically discovered from the database. The rough logic classifier uses lower and upper approximations for determining the class of the objects. The neural network is used for training the data, and has been used especially to avoid the boundary rules given by the rough sets that do not classify the data with cent percentage probability. Moreover, the algorithms based on the rough set theory are particularly suited for parallel processing architecture.