A solution to distinguish ventricular fibrillation and ventricular flutter from other arrhythmias and from disturbances caused by body motion or muscle activity with the use of a neural network has been investigated. Ventricular fibrillation and ventricular flutter occurs when the cardiac muscle cells are not triggered by the cardiac conduction system, but rather by ectopic foci preventing a synchronized contraction of the cardiac muscle cells and therefore inhibiting the hearts capability to properly pump blood. Two different methods, gradient descent and quasi-Newton, used by the network for learning was tested and preprocessing methods used on the input data before introducing it to the network was evaluated. Gradient descent makes use of the gradient to the error function with regards to its weights and updates the network in the direction which the output error by the network decreases the most. Quasi-Newton update the network roughly in the Newton direction by iteratively build up an approximation to the Hessian of the error function with the use of information from the gradient. The preprocessing methods used were: Threshold Crossing Intervals (TCI) which looks at the time between baseline crossings of the ECG signal. Mean Absolute Value (MAV) which computes the mean absolute value of the normalized ECG signal. Spectral Analysis which takes into account different properties of the frequency spectrum of ventricular fibrillation and normal sinus rhythm. VF-filter which assumes VF to be sinusoidal and computes the leakage after the ECG signal has been bandstop filtered around the mean frequency. Period and Amplitude Information of the maximum amplitude of the input frequency spectrum and its period. It was found that the networks that used the preprocessed signal was a poor classifier for the arrhythmias partially because ventricular fibrillation was not easily separable from the arrhythmias by the implementaion of the preprocessed inputs given.