Wiener filtering has many applications in the area of imaging science. In image processing, for instance, it is a common way of reducing Gaussian noise. In color science it is often used to estimate reflectances from camera response data on a pixel by pixel basis. Based on a priori assumptions the Wiener filter is the optimal linear filter in the sense of the minimal mean square error to the actual data. In this paper we propose a spatially adaptive Wiener filter to estimate reflectances from images captured by a multispectral camera. The filter estimates pixel noise using local spatial neighborhood and uses this knowledge to estimate a spectral reflectance. In the hypothetical case of a noiseless system, the spatially adaptive Wiener filter equals the standard Wiener filter for reflectance estimation. We present results of various simulation experiments conducted on a multispectral image database using a 6-channel acquisition system and different noise levels.