We prove that, assuming the Unique Games conjecture (UGC), every problem in the class of ordering constraint satisfaction problems (OCSPs) where each constraint has constant arity is approximation resistant. In other words, we show that if ρ is the expected fraction of constraints satisfied by a random ordering, then obtaining a p′ approximation for any p′ > ρ is UG-hard. For the simplest OCSP, the Maximum Acyclic Subgraph (MAS) problem, this implies that obtaining a ρ-approximation for any constant ρ > 1/2 is UG-hard. Specifically, for every constant ε > 0 the following holds: given a directed graph G that has an acyclic subgraph consisting of a fraction (1ρε) of its edges, it is UG-hard to find one with more than (1/2 + ε) of its edges. Note that it is trivial to find an acyclic subgraph with 1/2 the edges by taking either the forward or backward edges in an arbitrary ordering of the vertices of G. The MAS problem has been well studied, and beating the random ordering for MAS has been a basic open problem. An OCSP of arity k is specified by a subset π ⊆ Sk of permutations on {1, 2, ⋯ , k}. An instance of such an OCSP is a set V and a collection of constraints, each of which is an ordered k-tuple of V . The objective is to find a global linear ordering of V while maximizing the number of constraints ordered as in ρ. A random ordering of V is expected to satisfy a ρ = \π\k! fraction. We show that, for any fixed k, it is hard to obtain a p′-approximation for π-OCSP for any p′ ρ. The result is in fact stronger: we show that for every λ ⊆ π ⊆ Sk, and an arbitrarily small ε, it is hard to distinguish instances where a (1 - ε) fraction of the constraints can be ordered according to λ from instances where at most a (1 + ε) fraction can be ordered as in π. A special case of our result is that the Betweenness problem is hard to approximate beyond a factor 1/3. The results naturally generalize to OCSPs which assign a payoff to the different permutations. Finally, our results imply (unconditionally) that a simple semidefinite relaxation for MAS does not suffice to obtain a better approximation. © 2011 Society for Industrial and Applied Mathematics.