This paper shows that the weights of continuous-time feedback neural networks are uniquely
identifiable from input/output measurements. Under very weak genericity assumptions, the
following is true: Assume given two nets, whose neurons all have the same nonlinear activation
function oe; if the two nets have equal behaviors as \black boxes" then necessarily they must
have the same number of neurons and |except at most for sign reversals at each node| the
same weights.