Multimodal neural networks are models that learn concepts in multiple modalities. The models can perform tasks like zero-shot classification: associating images with textual labels without specific training. This promises both easier and more flexible use of digital photo archives, e.g. annotating and searching. We investigate whether existing multimodal models can perform these tasks, when the data differs from the typical computer vision training sets, on historical photos from a cultural context outside the English speaking world.