This paper presents a mechanotransduction model designed to convert the multi-axial mechanical loads at the fingertip-contact interface into neural-spike trains, the MultiAxial Stress Mechanotransduction (MASM) model. Seeking a comprehensive solution and more direct integration with sensor systems in tactile applications, the model accounts for the conversion of multi-axial (pressure and shear) stresses at the fingertip-contact interface into spike trains with artificial slow adapting (SA) and rapidly adapting (RA) afferents type I (SAI, RAI) and II (SAII, RAII). These have been modelled based on the properties of those in human fingertips. To illustrate how the model works, artificial data mimicking typical stress stimuli profiles used to evaluate the response of biological afferents were fed to the model and results examined. Subsequently, the suitability of the model for real tactile applications was preliminary tested by inputting to the model real life, measured pressure and shear data in a fingertip 'press-push-lift' action. The response of the modelled afferents was analyzed and qualitatively compared to typical responses of biological units. Initial results show that it is possible to codify the mechanical contact tactile information measured by multi-axial sensor systems in a bio-inspired fashion, reproducing relevant features similar to those produced by biological mechanoreceptors.