This work presents a methodology to improve soft sensors performances in spatial forecast of environmental parameters. To this aim, we substitute a single soft sensor based on a single neural network with a more complex connectionist system that we call the HyperSensor. HyperSensor is built by a set of soft sensors; each one based on a specific neural model and a gating neural network, which plays the role of a stochastic selector. HyperSensor wraps the best characteristics of different neural network models through the gating network, which selects the best performing soft sensor according to the current input. In other words, HyperSensor is able to independently choose the best instrument of measure to get the best performance.