Auscultation, which means listening to heart sounds, is one of the most basic medical methods used by physicians to diagnose heart diseases. These voices provide considerable information about the pathological cardiac condition of arrhythmia, valve disorders, heart failure and other heart conditions. This is why cardiac sounds have a great prominence in the early diagnosis of cardiovascular disease. Heart sounds mainly have two main components, S1 and S2. These components need to be well identified to diagnose heart conditions easily and accurately. In this case, the segmentation of heart sounds comes into play and naturally a lot of work has been done in this regard. The first step in the automatic analysis of heart sounds is the segmentation of heart sound signals. Correct detection of heart sounds components is crucial for correct identification of systolic or diastolic regions. Thus, the pathological conditions in these regions can be clearly demonstrated. In previous studies, frequency domain studies such as Shannon energy and Hilbert transformation method were generally performed for segmentation of heart sounds. These methods involve quite long and exhausting stages. For this reason, in this study, a re-sampled energy method which can easily segment heart sounds in the time domain has been developed. The results obtained from the experiments show that the proposed method segments S1 and S2 sounds very efficiently.