N-dimensional vectors (with large values of N) are widely used in information retrieval, where documents and text queries are represented as vectors, with one dimension per word. This is called the Vector space model. In this model, a key relevance metric is the cosine similarity (i.e., the cosine of the angle between the vector representing the query and the vector representing the document). As the angle decreases, the cosine approaches the maximum value of 1, and so does the relevance of the document to the query.
