This study was carried out in order to investigate the influence of frozen wood when calibrating a prediction model for the moisture content and density distribution of Scots pine (Pinus sylvestris) and birch (Betula pubescens) using microwave sensors. The material was initially of green moisture content, and thereafter dried to zero moisture content. At each step all the pieces were weighed, scanned with a microwave sensor (Satimo 9, 4 GHz), and CT scanned with a medical CT scanner (Siemens Somatom At.T.) at frozen and room temperature conditions. The output variables from the microwave sensor were used as predictors, and CT images correlated with known moisture content, temperature levels, and frozen/non-frozen conditions were used as response variables. Multivariate models to predict average moisture content and density were calibrated using PLS regression. The models for average moisture content and density were applied on mean values for spatially distributed areas and pixel level, and the distribution was visualized. The result shows that it is possible to predict both moisture content distribution and density distribution with high accuracy using microwave sensors, but frozen conditions require calibration.