The purpose of a tracking algorithm is to associate data measured by one or more (moving) sensors to moving objects in the environment. The state of these objects that can be estimated with the tracking process depends on the type of data that is provided by these sensors. It is discussed how the tracking algorithm can adapt itself, depending on the provided data, to improve data association. The core of the tracking algorithm is an extended Kalman filter using multiple hypotheses for contact to track association. Examples of various sensor suites of radars, electro-optic sensors and acoustic sensors are presented.