  The India-based Neutrino Observatory (INO) collaboration, as part of its
detector R\&D program, has developed prototype stacks of resistive plate
chambers (RPCs) to study their performance. These stacks have also been used as
testbenches for the development of related hardware and software. A crucial
parameter in the characterisation of these detectors and other physics studies
is the detection efficiency, which is estimated from track fitting of cosmic
muons passing through the stack. So far, a simple straight line fit was used
for track fitting, which was sensitive to noise hits and led to rejection of
events. In this paper, we present our first results of using artificial neural
networks (ANN) for track fitting of cosmic muons traversing a stack of RPCs. We
present in detail, the simulation framework designed for this purpose and show
that ANN offers better track reconstruction efficiency than straight line
fitting. We also discuss the influence of noise and detection efficiency of
cosmic muons on the track reconstruction efficiency.
