An efficient method to detect snoring and related events (expiration, inspiration and silence) in sleep sound recordings is proposed in this paper. The feature vector is obtained using normalized mean and standard deviation of 2 sub-bands energy. The proposed method is based on the acoustic properties of snoring sound which have been validated to be effective for snoring detection by our experiments. Then the classification procedure is done by applying Support Vector Machine. An approximately 32 hours’ database were recorded from the subjects who have acknowledged snoring habit. The performance of our method is evaluated by classifying the different events in sleep sound recordings and comparing with the ground truth. This algorithm was able to correctly classify the snores with the accuracy of 97.00%, 96.35% for breath and 99.80% for silence.