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A system to resolve an intramuscular electromyographic (EMG) signal into its component motor unit potential trains (MUPTs) is presented. The system is intended mainly for clinical applications where several physiological parameters of motor units (MUs), such as their motor unit potential (MUP) templates and mean firing rates, are of interest. The system filters an EMG signal, detects MUPs, and clusters and classifies the detected MUPs into MUPTs. Clustering is partially based on the K-means algorithm, and the supervised classification is implemented using a certainty-based algorithm. Both clustering and supervised classification algorithms use MUP shape and MU firing pattern information along with signal dependent assignment criteria to obtain robust performance across a variety of EMG signals. During classification, the validity of extracted MUPTs are determined using several supervised classifiers; invalid trains are corrected and the assignment threshold for each train is adjusted based on the estimated validity (i.e., adaptive classification). Performance of the developed system in terms of accuracy (Ae), assignment rate (Ar), correct classification rate (CCr), and the error in estimating the number of MUPTs represented in the set of detected MUPs (ENMUPTs) was evaluated using 32 simulated and 30 real EMG signals comprised of 3-11 and 3-15 MUPTs, respectively. The developed system, with average CCr of 86.4% for simulated and 96.4% for real data, outperformed a previously developed EMG decomposition system, with average CCr of 71.6% and 89.7% for simulated and real data, by 14.7% and 6.7%, respectively. In terms of ENMUPTs, the new system, with average ENMUPTs of 0.3 and 0.2 for simulated and real data respectively, was better able to estimate the number of MUPTs represented in a set of detected MUPs than the previous system, with average ENMUPTs of 2.2 and 0.8 for- simulated and real data respectively. For both the simulated and real data used, variations in Ac, Ar, and ENMUPTs for the newly developed system were lower than for the previous system, which demonstrates that the new system can successfully adjust the assignment criteria based on the characteristics of a given signal to achieve robust performance across a wide variety of EMG signals, which is of paramount importance for successfully promoting the clinical application of EMG signal decomposition techniques.