["The Journal of Physiology, EarlyView. ", "\nAbstract figure legend The goal of this study was to build an algorithm for machine‐learning classification of motor unit (MU) physiological type in the adult mouse. We made intracellular recordings of triceps surae (TS) motoneurons in anaesthetized adult mice and recorded MU isometric force and electromyographic (EMG) activity. We analysed and paired motoneuron electrophysiology with MU contractile phenotype; MUs were classified into four physiological types (slow, S; fast fatigue‐resistant, FR; fast intermediate, FI; or fast fatigable, FF) using unbiased clustering and supervised machine learning. A multinomial logistic regression model predicted MU type from a motoneuron electrical signature consisting of input conductance, rheobase, afterhyperpolarization (AHP) duration and maximal frequency with high accuracy. This work provides a quantitative description of MU properties in the adult mouse and establishes a predictive statistical model capable of predicting MU physiological type solely based on electrophysiology. Graphical abstract created using BioRender.com.\n\n\n\n\n\n\n\n\n\nAbstract\nMotor unit diversity arises from differences in the contractile properties of muscle fibres and the intrinsic electrical properties of their motoneurons. In mice, however, this relationship has not been quantitatively defined, and conventional classification often relies on subjective thresholds. Here, we combine in vivo intracellular recordings with supervised and unsupervised machine‐learning methods to test whether motoneuron electrophysiology can predict the physiological identity of mouse motor units. Unbiased clustering identified four groups corresponding to slow (S), fast fatigue‐resistant (FR), intermediate (FI) and fast fatigable (FF) types. A multinomial logistic regression model performed well, with most errors occurring between FI and FF types, which showed substantial overlap. Reducing the task to three classes improved accuracy. Feature selection revealed that four electrophysiological properties (input conductance, rheobase, afterhyperpolarization duration and maximal frequency) were sufficient for high predictive performance. Overall, this study provides a quantitative description of mouse motor unit properties and a framework for incorporating motor unit diversity into future investigations of neuromuscular physiology and disease.\n\n\n\n\n\n\n\n\n\nKey points\n\nMotor units are traditionally classified as slow (S), fast fatigue‐resistant (FR), fast intermediate (FI) or fast fatigable (FF) based on a handful of contractile properties, but in mice this classification has relied largely on subjective thresholds.\nWe used unsupervised clustering of 40 contractile variables recorded in vivo to define motor unit types objectively in the adult mouse triceps surae.\nMotoneuron electrophysiological properties, including input conductance, rheobase, afterhyperpolarization duration and firing frequency, systematically varied across identified motor unit types.\nA multinomial logistic regression model predicted motor unit type from motoneuron electrical properties with good accuracy, particularly for slow and fast fatigue‐resistant units.\nThese results establish quantitative criteria linking motoneuron excitability to muscle contractile phenotype in mice, providing a framework for studying motor unit diversity in health and neuromuscular disease.\n\n\n"]