This paper advocates the use of shape descriptors based on moments of 3D coordinates for morphometry of the cortical sulci. These descriptors, which have been introduced more than a decade ago, are invariant relatively to rotations, symmetry and scale and can be computed for any topology. A rapid insight of the derivation of these invariants is proposed first. Then, their potential to characterize shapes is shown from a principal component analysis of the 12 first invariants computed for 12 different deep brain structures manually drawn from 7 different brains. Finally, these invariants are used to find some correlates of handedness among the shapes of 116 different cortical sulci automatically identified in 144 brains of the ICBM database.