We present an analysis of the pointwise relationship between the reﬂectance of print and the surface topography of the paper before printing. We have measured the surface topography and reﬂectance of paper before and after printing in a sheet-fed pilot offset printing press. The 2D measurement maps have been aligned to obtain local print reﬂectance and surface topography values for every spatial position on the samples. In contrast to the various deterministic modeling approaches, which imply an a priori deﬁned underlying mathematical model, we apply probabilistic analysis. Therefore we ﬁrst estimate joint probability density functions (pdfs) of local topography and print reﬂectance using Gaussian Mixture Models (GMMs). From these pdfs we select paper regions with unusual properties, i.e. regions from the tails of the pdfs. These anomaly maps are analyzed for interrelations between the print reﬂectance and surface topography, its gradient and local variance. The degree of interrelation is characterized by the mutual information (MI), a measure to quantify statistical dependence without making assumptions about the linear or nonlinear nature of the regression dependence. The signiﬁcance of the MI values is conﬁrmed by simulation based statistical hypothesis testing. The objective is to offer answers to the question: How does the observation of an exceptional topography point on the paper surface change our information about whether the print quality attainable at that point will be exceptional or not? The results suggest that topography in combination with its local variance have the most prominent interrelation to small scale print anomalies. Furthermore it is shown that regions with abnormal topography have at least ten-fold higher probability to exhibit exceptionally high print reﬂectance, compared to randomly selected regions.