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Understanding neurodegenerative disease pathology requires a close examination of neurons and their processes. However, image-based single-cell analyses of neurons often require laborious and time-consuming manual classification tasks. Here, we present a machine learning (ML) approach leveraging convolutional neural network (CNN) classifiers capable of accurately identifying various classes of neuronal images, including single neurons. We developed the Single Neuron Identification Model 20-Class (SNIM20) which was trained on a dataset of induced pluripotent stem cell (iPSC)-derived motor neurons, containing over 12,000 images from 20 distinct classes. SNIM20 is built in TensorFlow and trained on images of neurons differentiated from iPSC cultures that were stained for nuclei and microtubules. This classifier demonstrated high predictive accuracy (AUC = 0.99) for distinguishing single neurons. Additionally, the 2-stage training framework can be used more broadly for cellular classification tasks. A variation was successfully trained on images of a human osteosarcoma cell line (U2OS) for single-cell classification (AUC = 0.99). While this framework was primarily designed for single-cell microraft-based identification and capture, it also works with cells in standard plate formats. We additionally explore the impact of fluorescent channels and brightfield images, class groupings, and transfer learning on the quality of the classification. This framework can both assist in high throughput neuronal or cellular identification and be used to train a custom classifier for the user’s specific needs.