Machine Translation (MT) plays an important role in our day to day life. It helps to bridge the communication gap. This research work shows the development of an Example-Based Machine Translation engine capable of converting Hindi texts into Sindhi Devanagari. A manually translated corpus of 50,000 sentences was used. The system has used vectorisation of sentences to match translations via cosine similarity. Performance of the proposed system was evaluated over a set of 1000 sentences and various translation quality metrics including BLEU, METEOR, TER, chrF and chrF++. We then compared the reported results against those from a Statistical Machine Translation (SMT) system we developed, which gives us both higher accuracy and fluency than the EBMT system.