Fibrous plaster (FP) is a composite material that has been used for suspended decorative ceilings in historical buildings since the late 19th century. Such ceilings are susceptible to failures, particularly in hard-to-access structural hangers (‘wads’). Due to a limited understanding of structural behaviour of FP ceilings, maintenance and reconstruction decisions are based on visual and tactile inspections. Such inspections are costly, intrusive and ultimately subjective. Non-destructive techniques are needed to detect and localise failures in FP ceilings. An Acoustic Emission system was recently developed to detect and localise damage sources in FP ceilings. The method processes data from AE sensor clusters with one-dimensional convolutional neural networks(1D-CNNs) trained to account for wave propagation anisotropy. This paper presents a case study conducted at the Institution of Civil Engineers (ICE) building in London (UK) to validate the method. The results confirm the ability of the method to localise failures under real site conditions in 450 mm-by-450 mm monitoring areas. The method may enable non-intrusive detection of failures from the underside of ceilings during periodic inspections. The possibility of conducting pre-training in laboratories and covering large monitoring areas up to 1500mm-by-1500mm is established and may facilitate practical applications in the future.