Automation tools for semiconductor defect data analysis are becoming necessary as device density and wafer sizes continue to increase. These tools are needed to efficiently and robustly process the increasing amounts of data to quickly characterize manufacturing processes and accelerate yield learning. An image-based method is presented for analyzing process 'signatures' from defect data distributions. This paper describes the statistical and morphological image processing methods used to achieve an automated segmentation of signature events into high-level process-oriented categories. Applications are presented for enhanced statistical process control, automatic process characterization, and intelligent subsampling of event distributions for off-line, high-resolution defect review.