ECG i.e Electrocardiogram represents electrical activity of the heart. When the ECG is abnormal, it is called arrhythmia. Millions of ECGs are taken for the diagnosis of various classes of patients, where ECG can provide a lot of information regarding the abnormality in the concerned patient, are analysed by the physicians and interpreted depending upon their experience. The interpretation may vary by physician to physician. Hence this work is all about the automation and consistency in the analysis of the ECG signals so that they must be diagnosed and interpreted accurately irrespective of the physicians. Many works have been done previously but this paper presents a new concept by application of MATLAB based tools in the same weighted neural network algorithms. This will help to reduce the hardware requirements, make network more reliable and thus a hope to make it feasible.To do so various networks were designed using the MATLAB based tools and parameters. Two classes of networks were designed, but with different training algorithms, namely Perceptron and Backpropagation. They were provided training inputs from the data obtained from the standard MIT-BIH Arrhythmia database.After training different forms of networks, they were tested by providing unknown inputs as patient data and the results in the whole process from training to testing were recorded in the form of tables. There are many types of abnormalities in ECGs like Ventricular Premature Beats, asystole, Couplet, Bigeminy, Fusion beats etc. In this paper only fusion beats have been discussed and so results associated with it only has been given, though the same principle was used to make networks for analyzing normal as well as ventricular premature beats too. The results for the fusion beats were best in the case of Feed Forward network algorithm. The percentage of correct classification is 96%. The results are compared with the previous work which concludes that the Feed Forward network with backpropagation Trainbfg algorithm is best for fusion beats classification.