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Let X be a topological space and F denote the Borel σ-field in X. A family of probability measures {Pλ} is said to obey the large deviation principle (LDP) with rate function I(⋅) if Pλ(A) can be suitably approximated by exp{−λinfx∈AI(x)} for appropriate sets A in F. Here the LDP is studied for probability measures induced by stochastic processes with stationary and independent increments which have no Gaussian component. It is assumed that the moment generating function of the increments exists and thus the sample paths of such stochastic processes lie in the space of functions of bounded variation. The LDP for such processes is obtained under the weak∗-topology. This covers a case which was ruled out in the earlier work of Varadhan (1966). As applications, the large deviation principle for the Poisson, Gamma and Dirichlet processes are obtained.