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Anomaly detection algorithms inspired by the natural immune system often use the negative selection metaphor to implement non-self detection. Much research has gone into ways of generating good sets of non-self detectors or antibodies and these methods’ time and space complexities. In this paper, the antibody morphology is defined as the collection of properties defining the shape, data-representation and data-ordering of an antibody. The effect these properties can have on self/non-self classification capabilities is investigated. First, a data-representation using fuzzy set theory is introduced. A comparison is made between the classification performance using fuzzy and m-ary data-representations using some benchmark machine learning data-sets from the UCI archive. The effects of an antigen data reordering mechanism based on Major Histocompatibility Complex (MHC) molecules is investigated. The population level effect this mechanism can have by reducing the number of holes in the antigen space is discussed and the importance of data order in the r-contiguous symbol match-rule is highlighted. Both are analysed quantitatively using some UCI data-sets.