With processing power of computers and capabilities of graphics devices increasing rapidly, the time is ripe to consider using hexagonal sampling for computer vision in earnest. This paper presents a framework for processing hexagonally sampled images. It concentrates on four key aspects in proposing a practical system which uses square sampled images as input. These are: conversion of square to hexagonally sampled images, storage, processing and display of hexagonally sampled images. Results from using this framework on some case studies show that the computational requirements for processing hexagonally sampled images are similar to conventional square sampled images. A comparison of the performance of hexagonal versus square sampling indicates that curves are represented with higher fidelity, with no need for higher pixel resolution, and aliasing errors are minimized with hexagonal sampling.