Background: The use of pulmonary function tests is primarily based on expert opinion and international guidelines. Current interpretation strategies are using predefined cutoffs for the description of a typical pattern. Objectives: We aimed to explore the predicted disease outcome based on the American Thoracic Society/European Respiratory Society (ATS/ERS) interpreting strategy. Subsequently, we investigated whether an unbiased machine learning framework integrating lung function with clinical variables may provide alternative decision trees resulting in a more accurate diagnosis. Methods: Our study included data from 968 subjects admitted for the first time to a pulmonary practice. The final clinical diagnosis was based on the combination of complete pulmonary function with the investigations that were decided at the physician's discretion. Clinical diagnoses were separated into 10 different groups and validated by an expert panel. Results: The ATS/ERS algorithm resulted in a correct diagnostic label in 38% of the subjects. Chronic obstructive pulmonary disease (COPD) was detected with an acceptable accuracy (74%), whereas all other diseases were poorly identified. The new data-based decision tree improved the general accuracy to 68% after 10-fold cross-validation when detecting the most common lung diseases, with a significantly higher positive predictive value and sensitivity for COPD, asthma, interstitial lung disease, and neuromuscular disorder (83/78, 66/82, 52/59, and 100/54%, respectively). Conclusions: Our data show that the current algorithms for lung function interpretation can be improved by a computer-based choice of lung function and clinical variables and their decision-making thresholds.