Normal cones provide powerful information about projections, tangent directions, and stopping conditions in constrained optimization. When the constraint set is defined through a collection of (well-behaved) analytic functions, normal cones are easily computed. In this talk we consider the situation where the constraint set is provided through an oracle function or collection of oracle functions. Methods for approximating normal cones under these conditions are provided and compared.