Verification means different modules to different people. To Roberts' initial attempt, the vision problem as a whole was a simple direct flow-chart. The verification step was the answer to the question, 'Within the vision system's capabilities, can this grouping of matches be identified (recognized) as this particular model?' There was no information feedback from this step to the other parts of the vision system, in either the case that a model was found or a hypothesis was wrongly generated. Indeed, much information can be derived at the verification stage. When we have gained sufficient confidence in a hypothesis describing image primitives (accept), then pose parameter refinement will point to additional model features in the image, which will be corroborated and later removed from the data so that other models in the image can be correctly recognized. (This includes other instances of the same model. Since we shall view all parameters in a similar manner, the instantiation of the model and camera viewpoint parameters defines the model; hence, each instantiation really does represent a different model.) If we reject individual matches then decisions must be made based on probabilistic error analysis. This will decide which part of the data was wrongly interpreted and which matches are generally inconsistent with the rest of the hypothesis generated. This paper presents current trends in verification vision systems and suggest an approach that considers all model parameters equally, easily extendable to generally curved models, and the incorporation of world knowledge.