Earthworms (Eudrilus eugeniae) have many benefits for the health and animal feed industries. The drying process of earthworms is necessary to extend their shelf life, yet conventional gravimetric moisture tests are slow and destructive. The purpose of this study was to classify the moisture content of earthworms using machine vision and artificial neural networks (ANN) during the drying process, with classified worms into wet (> 40% wb), semi-dry (40%–12%), and dry (< 12%) states. RGB images (n = 450) were acquired every 15 min during cabinet drying at 60 °C; reference moisture was obtained gravimetrically. Nine color and texture features were extracted and ranked in WEKA; then, the top eight features were retained. An external feed-forward ANN implemented in MATLAB with 8-40-3 architecture, TrainLM optimiser, logsig–logsig–purelin transfer functions yielded MSE = 0.0733 (training) and 0.086058 (validation) and R = 0.95309 (training) and 0.92962 (validation). The modest MSE gap reflects class imbalance rather than overfitting, as classification metrics on the unseen test set match the validation results.