The pulmonary artery catheter (PAC) has been used for decades in the diagnosis and treatment of critically ill patients, but knowledge of PAC waveform interpretation remains inadequate among physicians and nurses. Inspired by the relative success of EKG interpretation programs, this study investigates the feasibility of computerized PAC waveform interpretation. Clinician-provided contextual data, accompanying EKG data, and manually pre-processed waveform data were provided as input, and the ability of classifiers to recognize dangerous situations, system problems, waveform locations, and underlying patient physiology was evaluated. The dataset consisted of 66 waveforms classified by experts, and the classifiers tested were simple logistic regression, 1-nearest neighbor, decision tree, naïve Bayes, and neural network. Under 4-fold cross-validation, 1-nearest neighbor had the most success at classifying accurately, but the neural network had a high area under the receiver-operator curve more consistently across the four classification tasks. All classifiers were good at identifying location. The results of this feasibility study are encouraging and suggest that computerized PAC waveform interpretation may be useful to clinicians.