This paper proposes computationally light strategies to pre-process the sensor signals and extract features, feeding single layer feed-forward neural networks (SLFNNs) that proved good generalization performance keeping low the computational cost. We validate our proposal by integrating a tactile sensing system on a Baxter robot to collect and classify data from three objects of different stiffness. We compare different features extraction techniques and five SLFNNs to show the trade-off between generalization accuracy and computational cost of the whole processing unit.