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Container packages experience diverse forms of mechanical excitations during their transportation and handling. Some of these excitations can damage the goods and make them reach their final destination with defects. Early detection of damages to container packages during their transport on a container truck can allow the truck driver to take the necessary steps to avert larger damages and thus cause significant cost savings. Sensor based approaches are being investigated to provide this early detection capability. This paper presents an approach that uses wireless vibration sensors based on Zigbee protocol to monitor the integrity and safety of packages during transportation. T-mote Sky wireless nodes integrated with two 2-axis MEMS accelerometers were used to monitor the integrity of the packages. Experiments were conducted to discern the vibration patterns resulting from some common modes of mechanical stability losses, such as wobbling, tilting, colliding and sliding. The experiments used a 1:32 scaled version of RC truck and a proportionately sized container. The vibration patterns under multiple stability loss modes were captured. It was observed that each type of stability loss can be clearly classified based on the patterns in the vibration data collected. Extraneous signal components were suppressed using a wavelet analysis, and fidelity of the signals capturing the pattern associated with the stability loss events was enhanced. Thus, each stability loss is associated with a specific set of abnormal (out-of-control) behavioral patterns exhibited by the processes vibration signals. An interrogation and detection procedure was developed based on this wavelet analysis to detect in realtime the stability loss as well as the times and identities of the specific modes of stability loss that occurred during the span of time over which the measured data is collected. The results show that the multi-scale monitoring facilitated by the wavelet analysis of signals fro- m the wireless vibration sensor tags can be useful for accurate detection of stability loss events.