The goal of this study was to design a reliable method to quantify and visualize the anatomical distribution of cerebrospinal fluid (CSF) intracranially. The method should be clinically applicable and based on multispectral analysis of three-dimensional (3D) magnetic resonance images. T1-weighted, T2-weighted and proton density-weighted fast 3D gradient pulse sequences were used to form high resolution multispectral 3D images of the entire head. Training on single 2D slices, the Mahalanobis distances between the resulting multivariate tissue-specific densities were studied as functions of the feature vector composition and dimension. Multispectral analysis was applied to the images of four human brains. One feature vector with three components gave CSF volumes that were in the normal range and corresponding anatomical distributions that largely agreed with general anatomical knowledge. The exception was CSF missing around the basal parts of the brain due to signal artifacts. These artifacts were almost certainly due to the coil effect and magnetic field inhomogeneities induced by the imaged head. Such misclassifications could probably be reduced by bias field estimation and proper image restoration. Most CSF voxels formed large connected components that were found automatically, so the manual post-processing of the classified 3D image to locate CSF voxels was moderate. It is concluded that some of the fast, high resolution 3D gradient echo pulse sequences that have become available on conventional clinical scanners can be used to obtain good estimates of brain cerebrospinal fluid anatomical distribution and volume.