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M.Ing. / Nearest neighbour classifiers are well suited for use in practical pattern recognition applications for a number of reasons, including ease of implementation, rapid training, justifiable decisions and low computational load. However their generalisation performance is perceived to be inferior to that of more complex methods such as neural networks or support vector machines. Closer inspection shows however that the generalisation performance actually varies widely depending on the dataset used. On certain problems they outperform all other known classifiers while on others they fail dismally. In this thesis we allege that their sensitivity to the metric used is the reason for their mercurial performance. We also discuss some of the remedies for this problem that have been suggested in the past, most notably the variable-kernel similarity metric learning technique, and introduce our own extension to this technique. Finally these metric learning techniques are evaluated on an aircraft recognition task and critically compared.