This work deals with the design and implementation of soft sensors for a Sulfur Recovery Unit (SRU) in a refinery. Soft sensors are mathematical models able to emulate the behavior of existing sensors on the basis of available measurements. In this application, they are used when sensors are taken off for maintenance. The measurements considered in this work are very important for the environmental impact of the refinery, as they regard pollutant acid gas emissions. Four strategies have been implemented and compared: Multi-Layer Perceptrons (MLP) and Radial Basis Function neural networks, Neuro-Fuzzy networks and nonlinear Least-Squares (LSQ) fitting. The best performance is given by MLP neural networks and nonlinear LSQ, all of them implementing Nonlinear Moving Average models. The best soft sensors have been installed on the on-line distributed control systems of the refinery and on-line performance is highly satisfactory.