Automation systems strongly depend on the amount, quality, and richness of their sensor information. For decades, scientists have investigated towards more accurate and cheaper sensors as well as new sensors for previously undetectable properties or substances. With these enhancements the problem of too complex sensor information and sensor fusion raised. This paper is intended for giving a retrospection on perception systems in automation, followed by reviewing state-of-the-art approaches for handling diverse and complex sensor information as well as highlighting future requirements for more human-like systems that have the ability of performing their actions in complex and unpredictable environments. For the latter requirement, a section introducing a number of agent architectures for embedding the sensor fusion process into a comprehensive decision-making unit is given.