Independent Component Analysis (ICA) of images of natural scenes has been shown to generate basis functions, or filters, which resemble spatial [Bell & Sejnowski (1997). Vision Research, 37, 3327-3338; van Hateren & van der Schaaf (1998). Proceedings of the Royal Society of London B, 265, 359-366] and spatiotemporal [van Hateren & Ruderman (1998) Proceedings of the Royal Society of London B, 265, 2315-2320] receptive fields of simple cells of the striate cortex. ICA yields statistically independent components which provide for a redundancy-reduced representation of the data. Using one of several published algorithms [Lee (1998). Independent component analysis: theory and applications. Boston; Kluwer Academic], we applied linear ICA to color images of natural scenes. The resulting independent component filters (ICFs) separate into either luminance or color filters. The luminance filters are localized and oriented edge detectors as reported previously. The color filters resemble either blue-yellow or red-green double-opponent receptive fields with various orientations. An equal number of each type of filter (luminance, red-green, and blue-yellow) is obtained. Thus, ICA predicts that spatiochromatic information is coded in statistically independent luminance, blue-yellow, and red-green opponent pathways with a relatively equal representation and specific spatial profiles at the cortical level.