Mizo is an under-studied Tibeto-Burman tonal language of North-East India. Preliminary research findings have confirmed that four distinct tones of Mizo (High, Low, Rising, and Falling) appear in the language. In this work, an attempt is made to automatically recognize four phonological tones in Mizo distinctively using acoustic-prosodic parameters as features. Six features computed from Fundamental Frequency (F0) contours are considered, and two classifier models based on Support Vector Machine (SVM) & Deep Neural Network (DNN) are implemented for automatic tone recognition task respectively. The Mizo database consists of 31,950 iterations of syllables covering the four tones in Mizo, collected from 19 speakers using trisyllabic phrases. A four-way classification of tones is attempted with a balanced (equal number of iterations per tone category) dataset for each tone of Mizo. It is observed that the DNN-based classifier shows a performance in recognizing the four phonological tones in Mizo that is comparable to the SVM-based classifier. © European Language Resources Association (ELRA), licensed under CC-BY-NC