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Understanding the context of any phrase or extracting relationships requires part of speech tagging (POS). This article proposes an RNN-based POS tagger and compares its performance with some of the existing POS tagging methods. We present novel LSTM-based RNN architecture for POS tagging. The study attempts to determine the usefulness of machine learning and deep learning techniques for tagging part-of-speech of words for the low-resource Hindi language, which is an Indo-Aryan language spoken mostly in India. During the experiments, different deep learning architecture (ANN and RNN) and machine learning methods (HMM, SVM, DT) have been used. A multi-representational treebank and an open-source dataset have been used for the performance analysis of the proposed framework. The experimental results in terms of macro-measured variables have shown better results compared to some state-of-the-art methods. [ABSTRACT FROM AUTHOR]