Neural networks are intended to be used in future nanoelectronic

systems since neural architectures seem to be robust against

malfunctioning elements and noise in their weights. In this paper we

analyze the fault-tolerance of Radial Basis Function networks to Stuck-

At-Faults at the trained weights and at the output of neurons. Moreover,

we determine upper bounds on the mean square error arising from these

faults.