This paper presents a novel approach to the estimation of a general class of dynamic nonlinear system models. The main contribution is the use of a tool from mathematical statistics, known as Fishers' identity, to establish how so-called ``particle smoothing'' methods may be employed to compute gradients of maximum-likelihood and associated prediction error cost criteria.