More than one third (13 million) of adults aged 65 and above fall each year in the United States. Developing automated systems that detect falls is an important goal for those working in the field of eldercare technology. We developed an acoustic fall detection system (FADE) that automatically recognizes falls using purely acoustic (sound) information. The main challenge of building a fall detection system is providing testing data, since, no matter how realistic the falls for training the system are, they can not fully replicate the real elder falls. To address this challenge, we developed a knowledge based system rather than a data driven one. The system uses fuzzy rules based on knowledge of the specific frequency fingerprint of a fall and on the height of the origin of the sound. The rules were implemented in a Mamdani fuzzy rule system. We tested our system in a pilot study that consisted of a set of 23 falls performed by a stunt actor during six sessions of about 15 minutes each (1.3 hours in total). We compared the results of the fuzzy rule system to the results obtained using a K-nearest neighbor (KNN) approach with cepstral features. While the fuzzy rule system did not perform as well as the KNN one in the low false alarm region, it had the advantage that it reached 100% detection rate.