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It has become increasingly apparent that perception cannot be treated in isolation from the response generation, firstly because a very high degree of integration is required between different levels of percepts and corresponding response primitives. Secondly, it turns out that the response to be produced at a given instance is as much dependent upon the state of the system, as the percepts impinging upon the system. The state of the system is in consequence the combination of the responses produced and the percepts associated with these responses. Thirdly, it has become apparent that many classical aspects of perception, such as geometry, probably do not belong to the percept domain of a Vision system, but to the response domain. There are not yet solutions available to all of these problems. In consequence, this overview will focus on what are considered crucial problems for the future, rather than on the solutions available today. It will discuss hierarchical architectures for combination of percept and response primitives, and the concept of combined percept-response invariances as important structural elements for Vision. It will be maintained that learning is essential to obtain the necessary exibility and adaptivity. In consequence, it will be argued that invariances for the purpose of vision are not geometrical but derived from the percept-response interaction with the environment. The issue of information representation becomes extremely important in distributed structures of the types foreseen, where uncertainty of information has to be stated for update of models and associated data.