A method is presented to enable probabilistic design based on manufacturing blade variation. The goal of Point Cloud to Parameter (P2P) process is to autonomously extract design parameters from measured, three-dimensional scans of turbine engine blade profiles. Rather than simply determining statistical field variation in the blade surface, these extracted design parameters relate back to the creation of the original blade geometry, such that they could be utilized to recreate the blade in a CAD program. Designers can then use these results to compare and quantify design intent dimensions with manufacturing dimensions, and similarly, the intended performance of the blade. Results are presented of initial tests performed on a measured point cloud of blisk geometry with 18 blades. The parameters extracted using the P2P algorithm are compared to the results of a mesh deformation effort performed on the same geometry and it is shown that the P2P parameters correlate well with FEA computed frequencies.