With the developing technology, speech recognition systems are getting more space in our daily lives. Sounds in our environment are not only pure speech. Because of this, it is important for cochlear implants, unmanned vehicles and security systems to be able to recognize other sounds. In this work, Mel-frequency cepstrum coefficients, one of the most widely used methods for feature extraction in speech recognition, applied to various nature and animal sounds. Because each sound does not have the same duration, dynamic time warping, one of the methods used in speech recognition, is preferred to classify the feature vectors. The difference in durations of sounds affects the lengths of the feature vectors. With dynamic time warping method, one can overcome these differences. One reference record and 10 test records obtained from 10 different sound sources. True classification rate is found as 88%.