A common desire in nanoscience is to describe the size and morphology of nanoparticles as observed from TEM images. Many times, this analysis is done manually, a lengthy process that is prone to errors and ambiguity in the measurements. While several research groups have reported excellent advances in machine-learned approaches to automated TEM image processing, the tools that they have developed often require specialized software or significant knowledge of coding. This state of affairs means that a majority of researchers in the field of nanoscience are not well-equipped to incorporate these advances into their normal workflows. In this tutorial, we describe how to use Weka segmentation within the free and open source program FIJI to automatically identify and characterize nanoparticles from TEM images. The approach we outline is not meant to discount the excellent results of groups working at the forefront of machine learning image analysis; rather, it is meant to bring similar tools to a broader audience by demonstrating how such processing can be done within the GUI-based interface of FIJIa program already commonly used within nanoscience research. We also discuss the advantages that arise from automatic processing of TEM images, including repeatability, time savings, the ability to process low-contrast images, and the additional types of characterization that can be performed following identification of particles. The overall goal is to provide an accessible tool that enables a more robust and repeatable analysis and descriptions of nanoparticles.