We describe a working multi-transputer stereo vision system which exploits various forms of parallelism in a number of visual competences upon a specialized hardware architecture that provides distributed video datapaths to the processor array. We exploit stereo and spatial parallelism to recover 3-D scene descriptions from passive stereo vision. To recognize and recover the positions of modelled objects, we exploit featural parallelism at both the object and sub-object level. We also describe a real-time object-tracking algorithm that exploits featural parallelism in the concurrent tracking of a set of object features. These competences have been integrated into a general-purpose architecture and we present some performance results and descriptions of the use of this vision engine in two application domains.