When the binary choice probability model is derived from a random utility maximization model, the choice probability for one alternative has the form F[V(z, θ)]. Here V(z, θ) is a given function of the exogenous variables z and unknown parameters θ, representing the systematic component of the utility difference, and F is the distribution function of the random component of the utility difference. This paper describes a method of estimating the parameters θ without assuming any functional form for the distribution function F, and proves that this estimator is consistent. F is also consistently estimated. The method uses maximum likelihood estimation in which the likelihood is maximized not only over the parameter θ but also over a space which contains all distribution functions.