A Visually Grounded Speech model is a neural model which is trained to embed image caption pairs closely together in a common embedding space. As a result, such a model can retrieve semantically related images given a speech caption and vice versa. The purpose of this research is to investigate whether and how a Visually Grounded Speech model can recognise individual words. Literature on Word Recognition in hu- mans, Automatic Speech Recognition and Visually Grounded Speech models was evaluated. Techniques used to analyse human speech processing, such as gating and priming, were taken as inspiration for the design of the experiments used in this thesis. Multiple aspects of words recognition were investigated through three experiments. Firstly, it was investigated whether the model can recognise individual words. Secondly, it was investigated whether the model can recognise words from a partial sequence of its phonemes. Thirdly, it was investigated how word recognition is affected by contextual information. The experiments show that the model is able to recognise words while not being supervised for that task, and that factors such as word frequency, the length of a word and the speaking rate affect word recognition. Furthermore, the experiments reveal that words can be recognised from a partial input of a word’s phoneme sequence as well, and that recognition is negatively influenced by word competition from the word initial cohort. Furthermore, the word recognition in context experiment reveals that contextual information can enhance the recognition of words which are recognised less well.