In this study, we combined the extension neural network type 2 (ENN2) with the chaos theory in the electrocardiogram (ECG) recognition system. The self-developed hardware measurement circuit and LabVIEW human–machine interface were used to measure and capture ECG signals. The master–slave chaos system was adopted to change the stored ECG data into a chaotic dynamic error distribution graph to obtain the chaotic eye coordinates of specific ECG signals. ENN2 was used for recognition. There were 36 research subjects. The first half of the data were measured using the signal capture circuit, while the second half were provided by the medical center of Massachusetts Institute of Technology (MIT). According to the results of analysis, the proposed method has a high accuracy when applied to the classification of ECG recognition, with a recognition rate of up to 89%. Hence, the automatic diagnosis ECG system designed in this study can effectively categorize irregular heart rhythms and reduce the huge labor cost for reading.