This paper presents a method to decompose multichannel long-term intramuscular electromyogram (EMG) signals. In contrast to existing decomposition methods which only support short registration periods or single-channel recordings of signals of constant muscle effort, the decomposition software EMG-LODEC (ElectroMyoGram LOng-term DEComposition) is especially designed for multichannel long-term recordings of signals of slight muscle movements. A wavelet-based, hierarchical cluster analysis algorithm estimates the number of classes [motor units (MUs)], distinguishes single MUAPs from superpositions, and sets up the shape of the template for each class. Using three channels and a weighted averaging method to track action potential (AP) shape changes improve the analysis. In the last step, nonclassified segments, i.e., segments containing superimposed APs, are decomposed into their units using class-mean signals. Based on experiments on simulated and long-term recorded EMG signals, our software is capable of providing reliable decompositions with satisfying accuracy. EMG-LODEC is suitable for the study of MU discharge patterns and recruitment order in healthy subjects and patients during long-term measurements. Index Terms--Cluster analysis, intramuscular EMG signal decomposition, long-term analyzing, supervised classification, wavelet features.