Various Bayesian network classifier learning algorithms are implemented in Weka [10].This note provides some user documentation and implementation details. Summary of main capabilities: _Structure learning of Bayesian networks using various hill climbing (K2, B, etc) and general purpose (simulated annealing, tabu search) algorithms. _Local score metrics implemented; Bayes, BDe, MDL, entropy, AIC. _Global score metrics implemented; leave one out cv, k-fold cv and cumulative cv. _Conditional independence based causal recovery algorithm available. _Parameter estimation using direct estimates and Bayesian model averaging. _GUI for easy inspection of Bayesian networks. _Part of Weka allowing systematic experiments to compare Bayes net performance with general purpose classi_ers like C4.5, nearest neighbor, support vector, etc. _Source code available under GPL allows for integration in other systems and makes it easy to extend.