Classification of patient samples is a crucial aspect of cancer diagnosis and treatment. We present a method for classifying samples by computational analysis of gene expression data. We consider the classification problem in two parts: class discovery and class prediction. Class discovery refers to the process of dividing samples into reproducible classes that have similar behavior or properties, while class prediction places new samples into already known classes. We describe a method for performing class prediction and illustrate its strength by correctly classifying bone marrow and blood samples from acute leukemia patients. We also describe how to use our predictor to validate newly discovered classes, and we demonstrate how this technique could have discovered the key distinctions among leukemias if they were not already known. This proof-of-concept experiment paves the way for a wealth of future work on the molecular classification and understanding of disease.