So far, it is mostly up to human experts to identify the microstructural features of interest within the experimental data. Often, it is not clear a priori what features relate to performance in the applied context. Once identified, one would like to quantify their number density, size distribution, chemical characteristics, and functional properties, in order to extract quantitative processing-microstructureproperty relationships that facilitate material design. To automate this process, pattern recognition algorithms are actively being developed , often specifically targeted at a particular experiment for a particular type of material. Upon success, they provide a secondary characterization of the material in a reproducible and scalable way. This becomes particularly attractive when combined with high-throughput experimentation to systematically explore a material space.