Evaluation of Online Machine Translating Engines: A Comparative Study of English into Urdu Machine Translation The study is about online machine translation systems and their comparative performance for English to Urdu language translation. Machine translation is an emerging field and is important to facilitate multilingual communication. In multilingual societies like Pakistan the demand for machine translation has increased sharply. Currently a number of online translators are providing automated translation services but do these tools provide accurate results and useful service is the subject that is under investigation. The current work is focused on evaluating the quality of four OMT systems, Google, Bing, Babylon and Worldlingo for English to Urdu translation through comparative analysis. Three types of source texts informative, expressive and operative written in English are selected by using Reiss’s (1997) text type classification (explained by Munday, 2001) for machine translation experimentation. The selected texts are experimented through each sample OMT engine and the obtained data is evaluated using the parameters and evaluation performa designed for the study. Help is taken from works of Hutchins and Somers (1992), Gaule and Josan (2012) and Halliday’s scale for accuracy and intelligibility used by Ellinder (2012) to design the theoretical framework and evaluation scale for the study. The study investigates the quality of sample machine translations through linguistic investigation, clarity evaluation, error analysis and comparative assessments. Evaluation process of the study is based on self perceived examination and 3rd person human evaluators’ observations. Using this collaborative evaluation technique quality of sample OMT engines is explored quantitatively through linguistic assessment at lexical and syntactic levels and through clarity assessment at semantic level. The strength and weaknesses of translation performance of sample OMT systems are also analyzed and discussed through error analysis of the experimented data. The obtained results from these three types of assessments are finally compared to develop arguments about performance of sample OMT engines. The results indicated marked differences in competency of sample OMT systems for English to Urdu translation.