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In this paper, a methodology for target discrimination utilizing wavelet-based and morphological feature extraction is proposed. The proposed methodology is implemented into a landmine classification decision system utilizing metal detector array data as input. The classification performances of a number of feature vectors composed of different combinations of feature elements are assessed. This is conducted using a Fuzzy ARTMAP neural network classifier and majority voting decision fusion. The classification classes trialled during processing are target type and burial depth, both combined and individually. The majority of the results achieve correct classification percentages of above 80% both prior to and after decision fusion, with generally higher accuracies and lower misclassification percentages achieved after decision fusion.