This paper describes the facilities available for knowledge discovery in databases using the TETRAD II program. While a year or two shy of state of the most advanced research on discovery, we believe this program provides the most flexible and reliable suite of procedures so far availabIe commercially for discovering causal structure, semiautomatically constructing Bayes networks, estimating parameters in such networks, and updating. The program can also be used to reduce the number of variable needed for classification or prediction, for example as a neural net preprocessor. The theoretical principles on which the program is based are described in detail in Spirtes, Glymour and Scheines (1993). Under assumptions described there, each of the search and discovery procedures we will describe have been proved to give correct information when statistical decisions are made correctly.