We demonstrate how the separable least-squares technique of Golub and Pereyra can be exploited in the identi#cation of both linear and non-linear systems based on the prediction error formulation. The model classes to be considered here are the output error model and innovations model in the linear case and the Wiener system in the non-linear case. This technique together with a suitable choice of parameterization allow us to solve, in the linear case, the associated optimization problem using only np parameters instead of the usual n#m + p#+mp parameters when canonical forms are used, where n, m and p denote respectively the number of states, inputs and outputs. We also prove under certain assumptions that the separable optimization method is numerically better conditioned than its non-separable counterpart. Successful operations of these identi#cation algorithms are demonstrated by applying them to two sets of industrial data: an industrial dryer in the linear case and a high purity ...