To investigate whether lung function in patients with chronic obstructive pulmonary disease (COPD) can be directly predicted using CT densitometric measures and assess the underlying prediction errors as compared with the traditional spirometry-based measures. A total of 600 CT examinations were collected from a COPD study. In addition to the entire lung volume, the extent of emphysema depicted in each CT examination was quantified using density mask analysis (densitometry). The partial least square regression was used for constructing the prediction model, where a repeated random split-sample validation was employed. For each split, we randomly selected 400 CT exams for training (regression) purpose and the remaining 200 exams for assessing performance in prediction of lung function (e.g., FEV1 and FEV1/FVC) and disease severity. The absolute and percentage errors as well as their standard deviations were computed. The averaged percentage errors in prediction of FEV1, FEV1/FVC%, TLC, RV/TLC% and DLco% predicted were 33%, 17%, 9%, 18% and 23%, respectively. When classifying the exams in terms of disease severity grades using the CT measures, 37% of the subjects were correctly classified with no error and 83% of the exams were either correctly classified or classified into immediate neighboring categories. The linear weighted kappa and quadratic weighted kappa were 0.54 (moderate agreement) and 0.72 (substantial agreement), respectively. Despite the existence of certain prediction errors in quantitative assessment of lung function, the CT densitometric measures could be used to relatively reliably classify disease severity grade of COPD patients in terms of GOLD.