In recent years, a new technology, allowing the measurements of the expression of thousands of genes simultaneously, has emerged in medicine. This method, called DNA microarray analysis, is today one of the most promising method in functional genomics. Fundamental patterns in gene expression are extracted by several clustering methods like: hierarchical clustering, self organizing maps and support vector machines. Changes in gene expression, as a response to changing environment conditions, diseases, drug treatment or chemotherapy medications, can be detected allowing insights into the dynamic of the genome. Microarrays seem to be an important tool for diagnosis of diseases at a molecular level. Applications are for example the improvement of diagnosis and treatment of cancer and the improvement of the effectiveness of drug treatment. In this introductory paper, we present the principles of DNA microarray experiments, selected clustering methods for gene expression analysis and the impact to clinical research.