The spectral sensitivity functions of a digital image sensor determine the sensor’s color response to scene-radiated light. Knowing these spectral sensitivity functions is very important for applications that require accurate color, such as computer vision. Traditional measurements of these functions are time consuming, and require expensive lab equipment to generate narrow-band monochromatic light. Previous works have shown that sensitivity curves can be estimated using images of a color checker chart with known spectral reflectances, using either numerical optimization or machine learning. However, previous works in the literature have not considered sensitivity functions for CFAs (color filter arrays) other than RGB, such as RCCB (Red Clear Blue) or RYYCy (Red Yellow Cyan). Non-RGB CFAs have been shown to be useful for automotive and security camera applications, especially in low light situations. We propose a machine learning method to estimate the sensitivity curves of sensors with non-RGB filters, in addition to the RGB filters addressed previously in the literature, using a single image of a color chart under unknown illumination. Including non-RGB filters makes the estimation problem much more challenging, since the resulting space of color filters is no longer modelled by simple Gaussian shapes.