The classification of the electrocardiogram (ECG) into different patho-physiological disease categories is a complex pattern recognition task. This paper presents an intelligent diagnosis system using hybrid approach of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. Wavelet-transform is used for effective feature extraction and ANFIS is considered for the classifier model. It can parameterise the incoming ECG signals and then classify them into eight major types for health reference: left bundle branch block (LBBB), normal sinus rhythm (NSR), pre-ventricular contraction (PVC), atrial fibrillation (AF), ventricular fibrillation (VF), complete heart block (CHB), ischemic dilated cardiomyopathy (ISCH) and sick sinus syndrome (SSS). The inclusion of adaptive neuro-fuzzy interface system (ANFIS) in the complex investigating algorithms yields very interesting recognition and classification capabilities across a broad spectrum of biomedical problem domains. The performance of the ANFIS model is evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals. Cross validation is used to measure the classifier performance. A testing classification accuracy of 95% is achieved which is a significant improvement.