Summary: The univariate Z.W. Birnbaum and S.C. Saunders distribution [J. Appl. Probab. 6, 319–327 (1969; Zbl 0209.49801)] has been used quite effectively to model positively skewed data, especially life time data and crack growth data. We introduce a bivariate Birnbaum-Saunders distribution which is an absolutely continuous distribution whose marginals are univariate Birnbaum-Saunders distributions. Different properties of this bivariate Birnbaum-Saunders distribution are then discussed. This new family has five unknown parameters and it is shown that the maximum likelihood estimators can be obtained by solving two nonlinear equations. We also propose simple modified moment estimators for the unknown parameters which are explicit and can therefore be used effectively as an initial guess for the computation of the maximum likelihood estimators. We then present the asymptotic distributions of the maximum likelihood estimators and use them to construct confidence intervals for the parameters. We also discuss likelihood ratio tests for some hypotheses of interest. Monte Carlo simulations are then carried out to examine the performance of the proposed estimators. Finally, a numerical data analysis is performed in order to illustrate all the methods of inference here discussed.