Gene expression datasets are being produced in increasing quantities and made available on the web. Several thousands of genes are usually measured for their mRNA expression levels per sample using Affymetrix gene chips and Stanford microarrays, for instance. Such datasets are normally separated into distinct, objectively measured classes, typically disease states or other objectively measured phenotypes. A major problem for current gene expression analysis is, given the disparity between the number of genes measured (typically, thousands) and number of individuals sampled (typically, dozens), how to identify the handful of genes which, individually or in combination, help classify individuals. Previous approaches when faced with the dimensionality of the problem have tended to use unsupervised or supervised techniques that result in smaller clusters of genes, but clusters by themselves do not yield classification rules. This is especially the case with temporal microarray data, which represents the activity of genes within a cell, tissue or organism over time. The expression levels of some genes at a particular time-point can be controlled by the expression levels of other genes at a previous time-point. It is the extraction of these temporal connections within the data that is of great interest to biomolecular scientists and researchers within the pharmaceutical industry. If these so-called gene networks can be found that explain disease inception and progression, drugs can be designed to target specific genes so that the disease either does not progress or is even eradicated from an individual. In this paper we describe novel experiments using single-layer artificial neural networks for modelling both non-temporal (classificatory) and temporal microarray data.