Abstract: The research expresses an artificial intelligent system that helps a physician to early heart diseases diagnosis based-ECG database. It benefits the properties of common neural networks methods and the Multichannel Adaptive Resonance Theory (MART). It is an Adaptive Resonance Theory (ART) based neural network for adaptive classification of multichannel signal patterns without prior supervised learning. The mechanical aspect and the electrical aspect that include ECG wave creation, sensors leads of the body and all type of noises are observed in term of their effects on the ECG signal analysis. Also, the Premature Ventricular Contraction disease (PVC) was dealt. The operation of MART was tested for diagnosing a set of real patterns (QRS interval of PVC disease) that were taken from many patients of Holter ECG Database. Then an off line method was used for learning the MART system. A MART system of two-channels is used to quantify the different changing reliabilities of the individual signal channels, in the same time the credibility parameter of the system algorithm is determined and then used during the PVC pattern classification, this results of reducing the creation of spurious or duplicate categories (major problem for ART-based classification of noisy channels), in the same time, it reduces the processing time to be just 0.124 sec. The accuracy and sensitivity of MART are improved to be 93.4 and 95.5%, respectively that make the MART a dedicated algorithm for biomedical signals.