The Jan/Feb issue of JAMIA contained an interesting series of articles about the automated identification of smoking status from medical discharge records. It profiled the comparative performance of 11 different systems for the classification of patient records into five general categories for smoking status. The various classification approaches used, such as Bayesian classifiers, natural language processing, support vector machines and neural networks, illustrated the rich and diverse set of algorithms used in automated text processing and classification today. Even more impressive was the performance of some of these systems, which, in certain aspects, approximated the gold standard.