In addition, since heat spreads in a diffusive manner, the thermal behavior of each element is influenced by its surroundings, so the same material can show different thermal behaviors in different surroundings. In addition, thermal diffusion also results in blurry images at longer measurement times. Lastly, in practice, the heating of fragile heritage objects can be prohibited or it can prove challenging to heat heritage objects in a uniform manner in order to reduce noise, as these are often heterogeneous in composition and/or irregular in volume. Until now, relatively few studies reported the use of IRT for inspecting stained glass windows. Existing publications focus on the adhesion of glass paints [4; 17], manufacturing defects [18] or discuss the comparative thermal behavior of the various materials present, such as enamels, grisilles, lead cames, and welds [18; 19; 20]. The reported methods for data analysis are visual inspections of the thermograms [4; 17; 18; 20], temperature plots [17; 19; 20] and principal component analysis (PCA ) [18]. Higher order statistical analysis of the thermograms has also been used successfully on other historical glasses to detect degradation due to ageing [21; 22]. Nevertheless, IRT holds two particular limitations for glass characterization. First, thermal excitation of glass is relatively inefficient as radiation sources commonly applied for IRT emit radiation in the range for which glass is largely transparent. Silica glass is mostly transparent from 0.2 um up to 3.5-4.0 um and can be considered opaque for wavelengths longer than 4.0 um [23]. An alternative would be to use hot air but in this case heating will be less uniform and this is impractical for measuring larger objects. Second, care should be taken with additional sources of radiation, external to the setup, such as the sun or other (moving) warm objects or bodies in the room. Intensity fluctuations of these unintentional sources during the measurements can readily introduce noise in the thermography measurements as glasses exhibit high reflectance at incident angles above 45\({}^{\circ}\). This would mainly be an issue for in-situ measurements [24; 25]. The goal of this study was to assess the potential of IRT to differentiate compositional types of glass panes in historical leaded-glass windows which were typically subject to several conservation campaigns, and this by means of a case study (see Experimental section). Discriminating these glass types allows recognizing non-original glass panes and estimating the number of interventions that have occurred in the past, which is of key importance for defining the appropriate conservation strategy. Traditionally, the most prevalent methods for this type of investigation are XRF and scanning electron microscopy equipped with an energy dispersive X-ray detector (SEM-EDS) [26; 27; 28]. These techniques require destructive sampling and are labor-intensive, therefore they are not carried out on a routine basis. In order to find different groupings of glass the thermography data was processed using k-means clustering and hierarchical clustering algorithms. Both are unsupervised learning techniques that are used to partition data into clusters. K-means clustering groups all observations into a predetermined number of clusters, k. On the other hand, hierarchical clustering groups the observations hierarchically into subsequently larger clusters until they all belong to the same all-encompassing cluster. Hierarchical clustering was applied on Raman data to classify the glass panes in colored, stained glass windows, based on their chemical properties [16], but not yet with thermography data. In the broader field of heritage science, only [PERSON] et al. reported K-means clustering on thermography data to detect defects on a number of heritage artifacts (i.e., a statue, a fresco, and a painting on canvas) [29].