This work focuses on the demonstration of previously achieved results in the automatic detection of laughter from natural discourses. In this work however, we would like to show a proof of concept for the online and on the fly recognition of laughter performing close to real-time. The goal of this work is to use a previously trained model of laughter in a modular process engine environment, which is currently under development overcoming known difficulties of pattern recognition and information fusion tasks, to detect laughter from a continuous microphone input.