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Modern elevators are equipped with closed-circuit television (CCTV) cameras to record videos for post-incident investigation rather than providing proactive event monitoring. While there are some attempts at automated video surveillance, events such as urinating, vandalism, and crimes that involved vulnerable targets may not exhibit significant visual cues. On contrary, such events are more discerning from audio cues. In this work, we propose a hierarchical audio-visual surveillance framework for elevators. Audio analytic module acts as the front line detector to monitor for such events. This means audio cue is the main determining source to infer the event occurrence. The secondary inference process involves queries to visual analytic module to build-up the evidences leading to event detection. We validate the performance of our system at a residential trial site and the initial results are promising.