We present an efficient algorithm for optimizing parameters of a speech recognizer aimed at obtaining maximum accuracy at a specified decoding speed. This algorithm is not tied to any particular decoding architecture or type of tunable parameter being used. It can also be applied to any performance metric (e.g. WER, keyword search or topic ID accuracy) and thus allows tuning to the target application. We demonstrate the effectiveness of this approach by tuning BBN’s Byblos recognizer to run at 15 times faster than real time while maximizing keyword search accuracy.