This paper presents an intellectual diagnosis system using hybrid approach of Adaptive Neuro-Fuzzy Inference System (ANFIS) model for classification of electrocardiogram (ECG) signals. This method based on using cubic spline wavelet-transform for analyzing the ECG signals and extracting the parameters related to dangerous cardiac arrhythmia. These parameters were used as input of ANFIS classifier, four major types of ECG signals they are Normal Sinus Rhythm (NSR), Pre-Ventricular Contraction (PVC), Atrial Fibrillation (AF), Ventricular Fibrillation (VF), and Ventricular FLUtter (VFLU). The inclusion of 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 was evaluated in terms of training performance and classification accuracies. The results emphasize that the proposed ANFIS model illustrate potential advantage in classifying the ECG signals. A testing classification accuracy of 96.52% is achieved. © 2012 Praise Worthy Prize S.r.l. - All rights reserved. © 2013 Elsevier B.V., All rights reserved.