In this work, we consider recognizing online handwritten Assamese word (an Indic script) using the hybrid deep neural network-hidden Markov model (DNN-HMM) framework. The recognition task is generally challenging since Assamese handwriting is mixed with the discrete and cursive writing style, and further complicated due to the placing of vowel/consonant modifier above and/or below already written character in delayed order. Also, as it contains large character set, there is lack of common definition of the basic unit (BU) for automatic recognition. As a step in this direction, first, we select 173 BU capable of characterizing 20K most frequent Assamese words. The HMMs are created for these selected BUs. Next, we create a lexicon for word recognition where each word is represented by all probable 'sequence of BUs' that constitute the word. The system is developed using the state-of-the-art Kaldi Automatic speech recognition toolkit, under large vocabulary word recognition framework and is evaluated on the lexicon of sizes 1K, 5k, 10K and 20K words. To the best of our knowledge, this paper is the first of its kind that considers online handwritten Assamese word instead of isolated characters. The experiments are conducted on locally collected Assamese word database, and a promising recognition performance is obtained, noting that the DNN-HMM framework outperforms the GMM-HMM system significantly. © 2018 IEEE.