Multiple outcome measures are often used in clinical research and practice. However, the use of multiple measures inflates the probability of a type I error. In this paper, we used factor analysis techniques to reduce multiple outcome measures to a lesser number of orthogonal dimensions. The data were obtained from 119 patients with chronic obstructive pulmonary disease. Each patient had measurements made of 28 variables, including multiple parameters of pulmonary function, exercise tolerance and gas exchange. Factor analysis using a maximum likelihood iterative solution was performed. The factors were then rotated to a varimax solution. The analysis yielded four meaningful factors: exercise tolerance, disease severity, lung volumes and flow rates. Exercise tolerance and disease severity were the most important factors accounting, respectively, for 44 and 13% of the common variance. For further analyses, these composite factors could be used or a representative clinical measure from each factor might be chosen. We conclude that many physiologic measures provide highly correlated information about chronic obstructive pulmonary disease patients. Factor analysis may help reduce these measures into a smaller number of reliable composites.