This paper focuses on the design of an efficient automated data interpretation system that is able to output its interpretation directly to describe the nature of contact with the tactile surface. The work integrates distributive tactile sensing technology, artificial neural networks (ANNs), and advanced FPGA-based application-specific real-time digital signal processing, to produce working systems for use in a steerable endoscope that retrieves shape and ‘touch’ information. Different configurations of the signal interpretation such as single and cascade neural networks were assessed. It was shown that the cascaded ANN architecture could achieve an overall accuracy of better than 94% when measuring clinically significant contact information. This technology has the potential to discriminate contact and palpation in minimal invasive surgery (MIS) tools.