This thesis documents the research that has led to advances in the Artificial Neural Network (ANN) approach to analysing flow cytometric data from phytoplankton cells. The superiority of radial basis function networks (RBF) over multi-layer perception networks (MLP), for data of this nature, has been established, and analysis of 62 marine species of phytoplankton represents an advancement in the number of classes investigated. The complexity and abundance of heterogeneous phytoplankton populations, renders an original multi-class network redundant each time a novel species is encountered. To encompass the additional species, the original multiclass network requires complete retraining, involving long optimisation procedures to be carried out by ANN scientists. An alternative multiple network approach presented (and compared to the multi-class network), allows identification of the expanse of real world data sets and the easy addition of new species. The structure comprises a number of pre-trained single species networks as the front end to a combinatorial decision process for determining species identification. The simplicity of the architecture, and of the subsequent data produced by the technique, allows scientists unfamiliar with ANNs to dynamically alter the species of interest as required, without the need for complete re-training. Kohonens Self Organising Map (SOM), capable of discovering its own classification scheme, indicated areas of discrepancy between flow cytometric signatures of some species and their respective morphological groupings. In an attempt to improve identification to taxonomic group or genus level by supervised networks, class labels more reflective of flow cytometric signatures must be introduced. Methods for boundary recognition and cluster distinction in the output space of the SOM have been investigated, directed towards the possibility of an alternative flow cytometric structuring system. Performance of the alternative multiple network approach was comparable to that of the original multi-class network when identifying data from various environmental and laboratory culturing conditions. Improved generalisation can be achieved through employment of optical characteristics more representative of those found in nature.