The present article provides a comprehensive review of the work carried out on developing PRESEMT, a hybrid language-independent machine translation (MT) methodology. This methodology has been designed to facilitate rapid creation of MT systems for unconstrained language pairs, setting the lowest possible requirements on specialised resources and tools. Given the limited availability of resources for many languages, only a very small bilingual corpus is required, while language modelling is performed by sampling a large target language (TL) monolingual corpus. The article summarises implementation decisions, using the Greek-English language pair as a test case. Evaluation results are reported, for both objective and subjective metrics. Finally, main error sources are identified and directions are described to improve this hybrid MT methodology.