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Pattern recognition principles have been applied to 200 sets of spirometric data obtained from pulmonary function laboratory patients. Each patient was classified by a pulmonary specialist as normal, restricted, or mildly, moderately, severely, or very severely obstructed. Each patient was represented by a five-element pattern vector consisting of forced vital capacity (FVC), forced expiratory volume in one second (FEV1), midmaximum flow rate (MMFR), and flow rates with 50 and 25 percent of the vital capacity remaining (VÂ¿50 and VÂ¿25) normalized by predicted values. By Karhunen-Loeve expansion techniques, this vector was reduced to a two-feature pattern vector with only a 6 percent residual mean square representation error. The more important feature essentially represented the average of the three flow rates, while the second feature depended on FVC and FEV1. Data were divided into training and testing sets, and using the former, a parametric Bayes classifier and one-and two-layer pair-wise Fisher linear classifiers, were designed to assign patterns described by the two derived features to one of the six categories. With the testing set, overall recognition rates were 81 to 82 percent, with most errors representing misclassifications within the four obstructive categories. If the four obstructive classes were considered as a single class, the recognition rate increased to about 94 percent.