In forensic applications of speaker recognition it is necessary to be able to specify a confidence level for a decision that two sets of recordings have been produced by the same speaker (or by different speakers). Forensic phoneticians are sometimes criticized because they find it impossible to provide 'hard' estimates of the confidence level of their expert opinions. This paper investigates to what extent the problem can be solved by deploying automatic speaker verification algorithms, to work alone or to support the work of forensic phoneticians. It is shown that, although heavily dependent on operating conditions, one of the advantages of automatic systems is that their performance is in fact measurable. We construct a confidence measure which takes into account the past performance of the automatic system, the operating conditions and the probative value of the speech evidence, as well as the non-speech evidence. It is very important to note that such a confidence measure will never lead to a fully automatic procedure, since it still requires human input to weigh the non-speech evidence as well as human explanation of the procedure followed, and, finally, human interpretation. However, when all conditions are met, this procedure is able to (1) provide an interpretative measure in the individual forensic case and (2) join together the strengths of the human interpretation of the non-speech evidence and the automatic interpretation of the speech evidence, so that finally the joint performance of human and machine is better than the performance of one of them in isolation.