The vision model described in this paper utilizes a competitive winner-take-all network with several biological inspired levels similar to the Neocognitron and the work of Frohn, Geiger and Singer with improvements. Their models are simple object detectors and they incorporate feature detector levels. In the current system many of these simple object detectors are used and each one presents spatial information that is used by the top complex structured object detector level. This top level is inspired from Patrick Winston's and others' A.I. work on learning and matching structural descriptions of complex objects. The synaptic connections at this level contain relational information such as right-of, left-of, in-front-of, must-be and must-not. The winner-take-all characteristic of each level allows even partially obscured objects to be recognized. Forbidden properties of an object can be implemented by inhibitory connections while essential properties are implemented by excitatory connections. In this way a small number of missing essential properties does not automatically rule out the recognition of an object while present forbidden properties quickly rule it out.