The natural immune system is a robust and powerful information process system that demonstrates features such as distributed control, parallel processing and adaptation or learning via experience. Artificial Immune Systems (AIS) are machine-learning algorithms that embody some of the principles and attempt to take advantages of the benefits of natural immune systems for use in tackling complex problem domains. The Artificial Immune Recognition System (AIRS) is one such supervised learning AIS that has shown significant success on broad range of classification problems. The focus of this work is the AIRS algorithm specifically the techniques history previous research and algorithm function. Competence with the AIRS algorithm is demonstrated in terms of theory and application. The AIRS algorithm is analysed from the perspective of reasonable design goals for an immune inspired AIS and a number of limitations and areas for improvement are identified. A number of original and borrowed augmentations simplifications and changes to the AIRS algorithm are then proposed to addresses the identified areas. A professional-level implementation of the AIRS algorithm is produced and is provided as a plug-in for the WEKA machine-learning workbench.