Automatic speaker recognition is the task of automatically determining the identity of a speaker from a recording of their voice sample. One of the most important steps of speaker recognition that significantly influences the speaker recognition performance is known as feature extraction. Acoustic features of speech have been researched by many researchers around the world, however, there is limited research conducted on African indigenous languages. This paper presents the effects of acoustic features of speech towards the performance of speaker recognition systems focusing on South African low-resourced languages. This study investigates three acoustic features of speech namely, Time-domain, Frequency-domain and Cepstral-domain features extracted from the National Centre for Human Language Technology (NCHLT) Sepedi speech data. The results show that the performance is poor for time-domain features and good for frequency-domain features and even better for cepstral-domain features. However, the combination of these three features resulted in a higher accuracy.