Recently many researches concerning heart sound analysis are being processed with development of digital signal processing and electronic components. But there are few researches about recognition of heart sound, especially full cardiac cycled heart sound. In this paper, three new recognition methods about full cardiac cycled heart sound were proposed. The first method recognizes the characteristics of heart sound by integrating important peaks and analyzing statistical variables in time domain. The second method builds a database by principal components analysis on training heart sound set in time domain. This database is used to recognize new input of heart sound. The third method builds the same sort of the database not in time domain but in time-frequency domain. We classify the heart sounds into seven classes such as normal (NO) class, pre-systolic murmur (PS) class, early systolic murmur (ES) class, late systolic murmur (LS) class, early diastolic murmur (ED) class, late diastolic murmur (LD) class and continuous murmur (CM) class. As a result, we could verify that the third method is better efficient to recognize the characteristics of heart sound than the others and also than any precedent research. The recognition rates of the third method are 100% for NO, 80% for PS and ES, 67% for LS, 93 for ED, 80% for LD and 30% for CM.