Microarray techniques have recently made it possible to monitor simultaneously the activity of thousands of genes. They offer new insights into the biology of a cell. However, the data produced by microarrays poses several challenges to overcome. One major task in the analysis of microarray data is to reveal structures in the data despite its large noise component. We used fuzzy c-means (FCM) clustering in this study to achieve a robust analysis of gene expression time-series. We address the issues of parameter selection and cluster validity. Using statistical models to simulate gene expression data, we show that FCM can detect genes belonging to different classes. This may open the way for the study of fine-structures in microarray data.