This article presents Part of Speech tagging for Nepali text using General Regression Neural Network (GRNN). The corpus is divided into two parts viz. training and testing. The network is trained and validated on both training and testing data. It is observed that 96.13% words are correctly being tagged on training set whereas 74.38% words are tagged correctly on testing data set using GRNN. The result is compared with the traditional Viterbi algorithm based on Hidden Markov Model. Viterbi algorithm yields 97.2% and 40% classification accuracies on training and testing data sets respectively. GRNN based POS Tagger is more consistent than the traditional Viterbi decoding technique.