This thesis examines the theoretical and practical problems associated with the application of Artificial Neural Networks (ANN). The research reported focuses upon the ANN model proposed by Kohonen, the self-organising Kohonen Feature Map (KFM). This thesis is divided into three main sections: theory, implementation and application. The theoretical section of this thesis examines the properties and limitations of the Kohonen Feature Map network and its derivative models. Based upon this examination a novel class of network models, the Adaptive Kohonen Feature Map (AKFM) model, is proposed to overcome specific limitations of the original model. By utilising these network models a hierarchical ANN architecture, the Hierarchical Adaptive Kohonen Feature Map (HAKFM) model, is proposed and its properties described. The second section of this thesis examines the implementation of ANN. The discussion is divided into two sections, software and hardware, with the emphasis being placed upon the former. In particular the methodologies required for the implementation of ANN systems are examined in detail. The effectiveness of these techniques are reported based upon experimental results gathered from the construction of several prototype systems and computer simulations. The final section of this thesis addresses the requirement to migrate ANN from the laboratory environments into the real-world. To address this requirement a methodology is outlined for the development of ANN based applications and systems. The methodology provides an integrated framework to assist with this process. The methodology is intended to support several different perspectives upon this multifaceted task, including those of the application engineers' and the project managers'. This methodology comprises several phases that each include a definition of an aim and objectives thus enabling the progress of the whole project to be monitored.