In many applications, ranging from computer animation to biology, one wants to quantify how similar two surfaces are to each other. In the last few years, the Gromov-Haussdorff distance has been applied to this problem; this gives good results, but turns out to be very heavy computationally. This talk proposes a different approach, in which (disk-like) 2-dimensional surfaces (typically embedded in 3-dimensional Euclidean space) are first mapped conformally to the unit disk, and the corresponding conformal densities are then compared via optimal mass transportation,. This mass transportation problem differs from the standard case in that we require the solution to be invariant under global Moebius transformations. The metric we construct also defines meaningful intrinsic distances between pairs of "patches" in the two surfaces, which allows automatic alignment of the surfaces. Numerical experiments on "real-life" surfaces to demonstrate possible applications in natural sciences will be shown as well. This is joint work with Yaron Lipman.