Monotonicity is a constraint which arises in many application domains.
We present a machine learning model, the monotonic network, for which 
monotonicity can be enforced exactly, by virtue of functional form.  
A straightforward method for implementing and training a monotonic
network is described. Monotonic networks are proven to be universal
approximators of continous, differentiable monotonic functions. We
apply monotonic networks to a real-world task in corporate bond
rating prediction and compare them to other approaches.