In this paper, we establish a link between maximizing (information-theoretic) entropy and the construction of polygonal interpolants. The determination of shape functions on n-gons (n>3) leads to a non-unique under-determined system of linear equations. The barycentric co-ordinates ϕi, which form a partition of unity, are associated with discrete probability measures, and the linear reproducing conditions are the counterpart of the expectations of a linear function. The ϕi are computed by maximizing the uncertainty H(ϕ1,ϕ2,…,ϕn)=−∑ ϕi logϕi, subject to the above constraints. The description is expository in nature, and the numerical results via the maximum entropy (MAXENT) formulation are compared to those obtained from a few distinct polygonal interpolants. The maximum entropy formulation leads to a feasible solution for ϕi in any convex or non-convex polygon. This study is an instance of the application of the maximum entropy principle, wherein least-biased inference is made on the basis of incomplete information. Copyright © 2004 John Wiley & Sons, Ltd.