Bird sound classification based on their vocalization has become a significant research field nowadays. Acoustic sound produced by the birds is very rich and used to detect their species. In earlier days ornithologist used to detect the bird species, but this manual recognition is costly and requires huge amount of time. With the advancement of machine learning and deep learning, classification of syllables has become more significant. Methods of automatic sound recognition consist of different stages, such as preprocessing of the input audio file, segmentation of syllables, feature extraction followed by classification. In this study the models used for audio classification are concisely reviewed. Identification becomes more challenging due to a huge similarity between different species. However, noise reduction from the audio files is possible using several machine learning models. Deep learning techniques are also an emerging field in the classification domain, which is discussed in this review. Using these models, it is possible for researchers to detect species or even individual bird from their vocalization which is more time efficient. This paper aims to deliver a review summary, and present guidelines for utilizing the broadly used machine learning techniques in order to identify the challenges as well as future research directions of bird song recognition systems. © 2020 The authors and IOS Press. All rights reserved.