Fly ash is a common admixture used in concrete and may constitute up to 50% by weight of the total binder material. Incorporation of fly ash in Portland-cement concrete is highly desirable due to technological, economic, and environmental benefits. This article demonstrates the use of artificial intelligence neural networks for the classification of fly ashes in to different groups. Kohonen's Self Organizing Feature Maps is used for the purpose. As chemical composition of fly ash is crucial in the performance of concrete, eight chemical attributes of fly ashes have been considered. The application of simple Kohonen's one-dimensional feature maps permitted to differentiate three main groups of fly ashes. Three one-dimensional feature maps of topology 8-16, 8-24 and 8-32 were explored. The overall classification result of 8-16 topology was found to be significant and encouraging. The data pertaining to 80 fly ash samples were collected from standard published works. The categorization was found to be excellent and compares well with Canadian Standard Association's [CSA A 3000] classification scheme.