Automatic speech recognition of real-life conversational speech is a precondition for building natural human-centered man-machine interfaces. Being able to extract speech utterances from real-life broadcast news audio streams and transcribing them with an overall word accuracy of 83% we are still faced with the problem of transcribing true conversational speech in real-life (i.e. bad) background conditions. The switchboard task focusses on the latter problem. The paper summarizes a set of experimental investigations on the switchboard corpus using the Philips LVCSR system.