The vertebrate immune system is a robust and powerful information process system that demonstrates features such as distributed control, parallel processing and adaptation and 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 Clonal Selection Algorithm (CLONALG) is one such system inspired by the clonal selection theory of acquired immunity which has shown success on broad range of engineering problem domains. The focus of this work is the CLONALG algorithm specifically the techniques history previous research and algorithm function. In addition this work seeks to take desirable elements from CLONALG implementations and devise a clonal selection based classification algorithm. Competence with the CLONALG algorithm is demonstrated in terms of theory and application. The algorithm is analysed from the perspective of desirable features for an immune inspired classifier and a number a new clonal selection inspired technique called the Clonal Selection Classification Algorithm (CSCA) is proposed designed specified and preliminary tested. The technique is shown to be robust self-tuning (in terms of resources used) insensitive to user parameters and competitive as a classification system on common problem domains. An implementation of the CSCA is provided as a plug-in for the WEKA machinelearning workbench as is a naïve implementation of the CLONALG algorithm for classification.