An international database of case reports, each one describing a possible case of adverse drug reactions (ADRs), is maintained by the Uppsala Monitoring Centre (UMC), for the WHO international program on drug safety monitoring. Each report can be seen as a row in a data matrix and consists of a number of variables, like drugs used, ADRs, and other patient data. The problem is to examine the database and ÿnd signiÿcant dependencies which might be signals of potentially important ADRs, to be investigated by clinical experts. We propose a method by which estimated frequencies of combinations of variables are compared with the frequencies that would be predicted assuming there were no dependencies. The estimates of signiÿcance are obtained with a Bayesian approach via the variance of posterior probability distributions. The posterior is obtained by fusing a prior distribution (Dirichlet of dimension 2 n-1 ) with a batch of data, which is also the prior used when the next batch of data arrives. To decide whether the joint probabilities of events are di erent from what would follow from the independence assumption, the "information component" log(Pij=(PiPj)) plays a crucial role, and one main technical contribution reported here is an e cient method to estimate this measure, as well as the variance of its posterior distribution, for large data matrices. The method we present is fundamentally an artiÿcial neural network denoted Bayesian conÿdence propagation neural network (BCPNN). We also demonstrate an e cient way of ÿnding complex dependencies. The method is now (autumn 1998) being routinely used to produce warning signals on new unexpected ADR associations.