Abstract This paper illustrates the architecture and working of a proposed multilingual machine translation system which is able to translate from English to Urdu and Hindi. The system applies translation rules based approach with artificial neural network.The efficient pattern matching and the ability of learning by examples makes neural networks suitable for implementation of a translation rule based machine translation system.This paper also describes the importance of machine translation systems and status of the languages in a multilingual country like India.Machine translation evaluation score for translation output obtained from the system has been calculated using various methods such as n-gram bleu score, F-measure, Meteor and precision, recall. The evaluation scores achieved by the system for around 500 Hinditest sentences are as: n-gram bleu score 0.5903; Metric for Evaluation of Translation with Explicit ORdering (METEOR) score achieved is 0.7956 and F- score of 0.7916 and for Urdu n-gram bleu score achieved by thesystem is 0.6054; METEOR score achieved is 0.8083 and F- score of 0.8250.

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[23] Waibel A., Jain A., McNair A., Saito H., Hauptmann A., and Tebelskis J., “JANUS: A Speech-to-Speech Translation System Using Connectionist and Symbolic Processing Strategies,” in Proceedings of International Conference on Acoustics, Speech, and Signal Processing, Toronto, pp. 793-796 1991. Shahnawaz Khan (Ph. D.) is currently working as an Assistant Professor in Saudi Electronic University, Saudi Arabia. He has received his Ph. D. in Computer Science from Indian Institute of Technology, Banaras Hindu University (IT-BHU), Varanasi, India. He has around 17 years of experience in teaching and research and software industry. He is a member of editorial board in. He has been editor-in-chief for 1 international conference proceedings and 1 national conference proceedings. He is author of more than 20 research papers in refereed journals and International conferences. He has supervised 5 Master dissertations. His research interests are in smartphone sensing, natural language processing, machine learning and image processing. Imran Usman received his BE degree in Software Engineering from Foundation University, Pakistan in 2003 and MS Computer System Engineering from Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Pakistan in 2006. He joined Pakistan Institute of Engineering and Applied Sciences as a research scholar and received his PhD degree in 2010. From 2009 to 2010 he served at Iqra University Islamabad, Pakistan as an Assistant Professor in Department of Computing and Technology. From 2010 to 2012 he served as Assistant Professor and Senior In-charge Graduate Program in the Department of Electrical Engineering at COMSATS Institute of Information Technology Islamabad, Pakistan. He is presently serving as Assistant Professor in College of Computing and Informatics, Saudi Electronic University, Kingdom of Saudi Arabia. His present research interests include machine learning, digital image processing, evolutionary computation and digital watermarking. Dr. Usman has a number of research papers to his credit and has supervised many BS, MS and PhD students. He has been awarded Research Productivity Award in 2011, 2012, 2013 and 2014 by COMSATS Institute