In the last ten years, significant progress has been made in understanding the first steps in visual processing. Thus, a large number of algorithms exist that locate edges, compute disparities, estimate motion fields and find discontinuities in depth, motion, color and intensity. However, the application of these algorithms to real-life vision problems has been less successful, mainly because the associated computational cost prevents real-time machine vision implementations on anything but large-scale expensive digital computers. We here review the use of analog, special-purpose vision hardware, integrating image acquisition with early vision algorithms on a single VLSI chip. Such circuits have been designed and successfully tested for edge detection, surface interpolation, computing optical flow and sensor fusion. Thus, it appears that real-time, small, power-lean and robust analog computers are making a limited comeback in the form of highly dedicated, smart vision chips.