A self-learning translation-engine brokering system characterizes a set of language-translation engines by associating each engine with values of a set of engine parameters. The system receives a request to translate text or speech input, along with a set of weightings that identify the relative importance of each engine parameter to the translation requester. The system formats the input into a quantifiable engine-agnostic form and performs an optimization procedure that finds the best fit between the source's weightings and the sets of engine parameters. The system directs the input to the best-fitting translation engine, receives the translated output from the selected engine, directs the output to the translation requester, and then determines how well the translated output meets user expectations. This feedback used to update the best-fitting engine's parametric values and to train the system to make more accurate selections in the future.