Previous studies of the ex vivo lung have suggested significant intersubject variability in lung lobe geometry. A quantitative description of normal lung lobe shape would therefore have value in improving the discrimination between normal population variability in shape and pathology. To quantify normal human lobe shape variability, a principal component analysis (PCA) was performed on high resolution computed tomography (HRCT) imaging of the lung at full inspiration. Volumetric imaging from 22 never-smoking subjects (10 female and 12 male) with normal lung function was included in the analysis. For each subject, an initial finite element mesh geometry was generated from a group of manually selected nodes that were placed at distinct anatomical locations on the lung surface. Each mesh used cubic shape functions to describe the surface curvilinearity, and the mesh was fitted to surface data for each lobe. A PCA was performed on the surface meshes for each lobe. Nine principal components (PCs) were sufficient to capture >90% of the normal variation in each of the five lobes. The analysis shows that lobe size can explain between 20% and 50% of intersubject variability, depending on the lobe considered. Diaphragm shape was the next most significant intersubject difference. When the influence of lung size difference is removed, the angle of the fissures becomes the most significant shape difference, and the variability in relative lobe size becomes important. We also show how a lobe from an independent subject can be projected onto the study population’s PCs, demonstrating potential for abnormalities in lobar geometry to be defined in a quantitative manner.