This paper presents the use of simulated ECG signals with knownchaotic and random noise combination for training of an Artificial NeuralNetwork (ANN) as a classification tool for analysis of chaotic ECGcomponents. Preliminary results show about 85% overall accuracy in the abilityto classify signals into two types of chaotic maps – logistic and Henon. Robustness to random noise is also presented. Future research in the form ofraw data analysis is proposed, and further features analysis is needed