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Kahn’s principle states that if each process in a dataflow network computes a continuous input/output function, then so does the entire network. Moreover, in that case the function computed by the network is the least fixed point of a continuous functional determined by the structure of the network and the functions computed by the individual processes. Previous attempts to generalize this principle in a straightforward way to “indeterminate” networks, in which processes need not compute functions, have been either too complex or have failed to give results consistent with operational semantics. In this paper, we give a simple, direct generalization of Kahn’s fixed-point principle to a large class of indeterminate dataflow networks, and we sketch a proof that results obtained by the generalized principle are in agreement with a natural operational semantics.