Statistical methods that implement numerical approximations to the Bivariate Normal (BN) distribution function require high accuracy. Most notable are those involving optimization (e.g., maximum likelihood and nonlinear least squares). Unfortunately high accuracy comes at the cost of increased computation time. For this reason, in the present paper we compare eight Bivariate Normal (BN) approximation algorithms with regard to the accuracy speed trade-off. The method developed by Divgi (1979) emerges as the clear method of choice, achieving 14-digit accuracy ten and a half times faster than its nearest competitor. Furthermore, in the time required by Divgi's approximation to reach this level of precision none of the other methods can support better than 3-digit accuracy.