The manufacturing of polyamide 12 (PA12) components by HP’s multi-jet fusion (MJF ™) technology is recently attracting more attention thanks to less residual stress accumulation and very good accuracy in terms of geometry and surface quality. In the present work, four build jobs are produced with 12 samples each, yielding 48 samples in total. In each job, the orientation of the specimens is varied parallel to the build plate, adopting two different specimen orientations. Within each build job the specimens are laterally distributed and arranged at two distinct height levels. Performing tensile tests at two different strain rates, material parameters are derived and analyzed using the Gaussian mixture clustering algorithm and a principal component analysis (PCA) for an analysis of the results. This approach demonstrates the potential of applying unsupervised learning methods to investigate process-property linkages. The Gaussian mixture clustering algorithm is run with two, four, and six clusters, respectively, in the same data set to identify four as a sensible number of clusters for subsequent analysis. The clustering algorithm is able to conclusively link the size of the cross section of the manufactured specimens to their respective positions on the build platform. For other material parameters, such as the Young’s modulus, the elongation at tensile strength, and the yield stress, no clustering with respect to the position in the build chamber is recognizable. The PCA yields continuous information regarding the variation of material parameters with respect to the position of the specimen on the build platform. Based on the PCA results, a different behavior of specimens in the two investigated orientations is identifiable.