Purpose: Quantitative description of breast shape with two parameters derived from a statistical methodology denominated principal component analysis (PCA). Methods: Creation of a heterogeneous dataset of breast shapes acquired with a commercial infrared three-dimensional scanner on which PCA was performed. Plotting on a Cartesian plane of the two highest values of PCA for each breast (principal component 1 and 2). Testing of the methodology on a pre-post op surgical case and test-retest performed by two operators. Results: The first two principal components derived from PCA are able to characterize the shape of the breast included in the dataset. The test-retest demonstrated that different operators are able to obtain very similar values of PCA. The system is also able to identify major changes in the pre-post op of a two-stage reconstruction. Even minor changes were correctly detected by the system. Conclusions: This methodology can reliably describe the shape of a breast. An expert operator and a newly trained one can reach similar results in a test re-testing validation. Once developed and after further validation it could be employed as a good tool for outcome evaluation, auditing and benchmarking.