Mosaic filter-on-chip CMOS sensors enable the parallel acquisition of spatial and spectral information. These mosaic sensors are characterized by spectral filters which are applied directly on the sensor pixel in a matrix which is multiplied in the x- and y-direction over the entire sensor surface. Current mosaic sensors for the visible wavelength range using 9 or 16 different spectral filters in 3×3 or 4×4 matrices. Methods for the reconstruction of spectral reflectance from multispectral resolving sensors have been developed. It is known that the spectral reflectance of most natural objects can be approximated with a limited number of spectral base functions. In these cases continuous spectral distributions can be reconstructed from multispectral data of a limited number of channels. This paper shows how continuous spectral distributions can be reconstructed using spectral reconstruction methods like Moore-Penrose pseudoinverse, Wiener estimation, Polynomial reconstruction and Reverse principal component analysis. These methods will be evaluated with monolithic mosaic sensors. The Goodness of Fit Coefficient and the CIE color difference are used to evaluate the reconstruction results.