This paper presents artificial neural network (ANN) models designed to predict ice in Canadian lakes and reservoirs during the early winter ice thickness growth period. The models fit ice thickness measurements at one or more monitored lakes and predict ice thickness during the growth period either at the same locations for dates without measurements (local ANN models) or at any site in the region (regional ANN model), provided that the required meteorological input variables are available. The input variables were selected after preliminary assessments and were adapted from time series of daily mean air temperature, rainfall, cloud cover, solar radiation, and average snow depth. The results of the ANN models compared well with those of the deterministic physics-driven Canadian Lake Ice Model (CLIMO) in terms of root-mean-square error and in terms of relative root-mean-square errors. The ANN models predictions were also marginally more precise than a revised version of Stefan's law (RSL), presented herein. They reproduced some intrawinter and interannual growth rate fluctuations that were not accounted for by RSL. The performance of the models results in good part from a careful choice of input variables, inspired from the work on deterministic models such as CLIMO. ANN models of ice thickness show good potential for the use in contexts where ad hoc adjustments are desirable because of the limited availability of measurements and where poor data nature, availability, and quality precludes using deterministic physics-driven models.