This paper deals with the online decomposition of intramuscular electromyographic (iEMG) signals. A Markov model is proposed, which takes into account a varying number of firing motor neurons. A Bayes filter detects online the firing motor units by using a dictionary of approximated motor unit action potentials waveforms, and estimates precisely the action potential shapes and the respective firing rates. The method was tested on both simulated and experimental signals.