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Radial basis function artificial neural networks (ANNs) were trained to discriminate between\udphytoplankton species based on 7 flow cytometric parameters measured on axenic cultures.\udComparison was made between the performance of networks restricted to using radially-symmetric\udbasis functions and networks using more general arbitrarily oriented ellipso~dal basis functions, with\udthe latter proving significantly superior in performance. ANNs trained on 62, 54 and 72 taxa identified\udthem with respectively 77, 73 and 70% overall success. As well as high success in identification, high\udconfidence of correct identification was also achieved. Misidentifications resulted from overlap of character\uddistributions. Improved overall identification success can be achieved by grouping together species\udwith similar character distributions. This can be done within genera or based on groupings indicated\udin dendrograms constructed for the data on all species. When an ANN trained on 1 data set was\udtested with data on cells grown under different light conditions, overall successful identification was\udlow (<20%), but when an ANN was trained on a combined data set identification success was high\ud(>?0%). Clearly it is essential to include data on cells covering the whole spectrum of biological variatlon.\udWays of obtaining data for training ANNs to identify phytoplankton from field samples are discussed

Identification of 72 phytoplankton species by radial basis function neural network analysis of flow cytometric data