Many imaging systems seek a good interpretation of the scene presented
--- i.e., a plausible (perhaps optimal) mapping from aspects of the scene
to real-world objects. This paper addresses the issue of how to find such
likely mappings"efficiently". In general, an ``(interpretation) policy''
specifies when to apply which ``imaging operators'',
which can range from low-level edge-detectors and region-growers through 
high-level token-combination--rules and expectation-driven object-detectors.
Given the costs of these operators and the distribution of possible images,
we can determine both the expected cost and expected accuracy
of any such policy. Our task is to find a maximally effective policy
--- typically one with sufficient accuracy, whose cost is minimal.
We explore this framework in several contexts, including the
eigenface approach to face recognition. Our results show, in particular,
that policies that select the operators that maximize
"information gain per unit cost" work more effectively than other policies,
including ones that, at each stage, simply try to establish the putative
most-likely interpretation.