In practice, multisensor systems use dissimilar sensors that may have different data rates. Such sensors may also have inherent delays due to multitasking as well as communication delays between the sensor platform and a remote central processing site. Track fusion algorithms are presented that are valid for asynchronous sensors (the sensors have different data rates and different delays) as well as synchronous sensors (all of the sensors take measurements at the same time and the same rate with no delays). The asynchronous track fusion problem is formulated and solved first. Then the synchronous track fusion problem is obtained as a special case of the asynchronous one. Finally, using simulated target tracks, the performance of the asynchronous track fusion (ASTF) algorithm is analyzed and compared to an existing track fusion algorithm. Different sensor data rates and communication delays are used in the simulations. It is found that the ASTF algorithm outperforms its counterpart and is able to handle relatively large communication delays. The results presented set the foundation for deriving optimal track fusion algorithms when taking into account realistic constraints such as sensors with different data rates and different communication and/or processing delays. The results can also be used as a benchmark to evaluate existing suboptimal track fusion algorithms.