This paper presents a monitoring application with a wireless sensor network that was performed on a 95 years old riveted steel railway bridge. In order to perform an accurate assessment, strains were monitored on critical elements to catch the real loading during the passage of heavy freight trains. The wireless sensor network deployed on the bridge consisted of 8 nodes supplied with resistance strain gages and the root node connected to a solar energy rechargeable, battery powered base station. The monitoring system was operated in event-based mode to achieve an energy efficient operation to prolong the lifetime of the sensor network. The event detection was carried out with ultra low power MEMS acceleration sensors, which measured continuously the accelerations of the bridge and detected an approaching train. If this occurred, the sensor generated an interrupt that immediately switched on the strain gage's conditioning board and starts the measurement. Switching on the conditioning board shortly before starting the measurement, however, produces biased raw data because the strain gage was still heating up due to the current flow. Instead of eliminating the time-dependent bias by adding a dummy gage to the Wheatstone bridge, the bias was removed by post-processing the raw data. The paper demonstrates that this procedure provides sufficiently accurate input data for use in cycle counting based fatigue assessment of steel bridges.