An integrated framework for ventricular arrhythmias (VA) assessment, composed of two levels, is proposed in this work. The first level consists of four independent neural networks (NN), designed for specific detection tasks: signal quality, premature ventricular contractions (PVC), ventricular tachycardia (VT) and ventricular fibrillation (VF). Time and frequency domain features, obtained from the electrocardiogram (ECG) and selected through a correlation analysis procedure, form the inputs to the neural modules. The outputs feed the second layer, which consists of a global classifier (ANFIS structure), returns the global result for the VA assessment scheme. Sensitivity and specificity values, evaluated from public MIT-BIH databases, show the effectiveness of the proposed strategy.