Spatial forecasting of physical environmental parameters like temperature and humidity, can be realized by soft sensors based on neural networks. The paper is focused on the use of an original neural network model named E-αNet to realize a soft sensors system. E-αNet introduces the concept of “automatic learning” of the activation functions, reducing the complexity of the net in terms of number of hidden units and improving the learning capability. A comparison among different architecture models led from statistical and metrological points of view, shows how E-αNet produces interesting results in a real world application (a non invasive monitoring of the conservation state of old monument).