Bayès Syndrome is manifested in the cardiac cycle of an electrocardiogram. It presents associations with multiple medical conditions, being of interest in its identification at an early stage. In this article, we applied the Hierarchical Clustering method, through Matlab implementation, to identify each signal in 4 groups or categories of interest for diagnosing Bayes Syndrome. Different values were configured for the method parameter. The best value was obtained with the 'ward' option with a normalized sample concerning signal amplitude and time, achieving a total f1 Score of 0.88. The performance of K-Means++ and FAUM from previous work was compared with the results obtained from Hierarchical Clustering for a sample with normalized amplitude. The total f1 Score indicator for Hierarchical Clustering was lower than the value obtained from the two K-Means++ implementations and higher than the adjusted FAUM value