As a part of 2016 Physionet/CinC callenge, this work aims at the detection of abnormal phonocardiogram (PCG) recordings. Heart sound signal analysis has been an active research topic over the past decades with various studies such as heart sound segmentation and classification. We used the Physionet/CinC2016 challenge PCG database, which contains a large public collection of PCG recordings from a variety of clinical and nonclinical environments. The PCG classification in this work is per- formed in two steps. PCG heartbeats are first segmented and various heart sound markers are delineated. Then, a series of beat-specific features are extracted from the segmented heartbeats. Finally, PCG recordings are classified into normal and abnormal groups by performing classification based on tape-long features and by analyzing beat-extracted features from the PCG. Our method achieved an overall score of 80 in the unofficial phase of the challenge. In the official phase, the overall score of the proposed method was 82, with a sensitivity of 89%.