machine learning

So I have a black box that is throwing streams of network data into two bins. I want to run a bayesian classifier (or some sort of classifier) to try and understand how it makes its choices. The input/output is easy.... I have parsers to break the problem into about 50 binary dimensions. But the problem is I want to tune a classifier and then understand the classification it comes up with in human-ish terms, not as some n-dimensional mapping based on quadratic functions or whatever the fuck you mathematicians do while wanking it.

No, I need actual things like "oie, it always goes bugger when the layer two protocol is six, mate!"

How does one do that?

any more info about the black box..? input layer size / type (binary inputs?) .. / hidden layer size?