When combining data from various sensors it is vital to acknowledge possible measurement delays. Furthermore, the sensor fusion algorithm, often a Kalman filter, should be modified in order to handle the delay. The paper examines different possibilities for handling delays and applies a new technique to a sensor fusion system for estimating the location of an autonomous guided vehicle. The system fuses encoder and vision measurements in an extended Kalman filter. Results from experiments in a real environment are reported