Increasing demand for mass screenings and current possibilities of automated image acquisition lead to an accumulation of large amounts of image data in diverse fields of biology, so that the manual measurement and handling of such image data sets is often no longer feasible. The authors’ work in diatom analysis represents one of the areas that are impacted by this trend. The newly developed tool SHERPA offers a versatile image processing workflow focused on the identification and measurement of diatom outlines, handling all steps from image segmentation over object identification to feature extraction, and providing interactive functions for reviewing and revising results. Special attention was given to the ease of use, applicability to a broad range of data and problems, and the minimization of manual intervention by extensive automating and internal quality control of the results. Though it was developed for analyzing images of diatom valves originating from automated slide scanning microscopy, SHERPA can also be useful for other object detection, segmentation and identification problems. Tested with several datasets from different sources and of various compositions, SHERPA proved its ability to successfully analyze large amounts of diatom micrographs depicting a broad range of species. It identifies relevant valve shapes and extracts features suitable for detailed morphometric analysis and classification. Ranking of results by template matching and quality criteria helps focusing manual inspection upon difficult cases, allowing for minimum user intervention as well as for maximum output, providing a helpful tool for high-throughput analyses of image data. By applying a workflow using digital imaging and automated diatom analysis large amounts of data can be processed, and morphometric trends can be detected easily.