jats:titleAbstract</jats:title>jats:pMorphometry (i.e., the quantitative determination of grain size and shape information) is an essential component of all rock and environmental magnetic studies. Electron microscopy is often used to image magnetic mineral grains, but the current lack of systematic image processing tools makes it challenging to quantify key morphological features of magnetic minerals in natural samples. Here, we present an easy‐to‐use machine learning framework jats:italicMagNet</jats:italic> for automated morphological recognition of magnetic mineral grains in microscopic images. This framework, based on a convolutional neural network, performs well in the recognition and classification of magnetofossil nanoparticles in transmission electron microscopy images after training and testing. jats:italicMagNet</jats:italic> is open‐source and can easily be extended to process different types of mineral images. This tool has the potential, therefore, to extract key quantitative information of magnetic mineral populations within heterogeneous terrestrial and meteoritic samples for the interpretations of Earth and planetary processes.</jats:p>