Abstract: A slab temperature neural network soft sensor model based on fuzzy clustering is studied. The approach consists of two components: an FCM (Fuzzy C-Means) clustering, which classifies training objects into a couple of clusters, and a distributed RBF (Radial Basis Function) network, which is used to train each cluster. In the online stage, the values of membership are computed using an adaptive fuzzy clustering algorithm for the new object. The proposed approach has been applied to the slab temperature estimation in an actual reheating furnace. Simulations show that the approach is effective.