The precision decomposition technique can accurately identify a significant number of action potential trains within intramuscular electromyographic (EMG) signals. The original version of this technique (PD I) often requires extensive user-interactive editing to improve upon the results from a maximum a-posteriori probability receiver (MAPR). We have used the integrated processing and understanding of signals methodology from artificial intelligence to formulate and implement a new multi-receiver solution that augments MAPR with two other receivers to gain greater accuracy. Specifically, each new receiver utilizes an interleaving of signal and symbol processing stages to address MAPR inadequacies in resolving cases of acute superposition and shape instability among motor unit trains. Prior to any user-interactive editing, our multi-receiver system achieves a classification accuracy of 85.1%, a significant improvement over the 66.0% accuracy of PD I on the same database of challenging EMG signals.