Attractor networks have been one of the most successful paradigms in neural computation, and have been used as models of computation in the nervous system. Many experimentally observed phenomena such as coherent population codes, contextual representations, and replay of learned neural activity patterns are explained w ell by attractor dynamics. Recently, we proposed a paradigm called “latent attractors” where attractors embedded in a recurrent network via Hebbian learning are used to channel network response to external input rather than becoming manifest themselves. This allows the network to generate context-sensitive internal codes in complex situations. Latent attractors are particularly helpful in explaining computations within the hippocampus a brain region of fundamental significance for memory and spatial learning. The performance of latent attractor networks depends on the number of such attractors that a network can sustain. Following methods developed for associative memory networks, we present analytical and computational results on the capacity of latent attractor networks.