Named entity recognition has been one of the most widely researched natural language processing technologies over the past two decades. For the South African languages, however, relatively little research and development work has been done. This changed with the release of the NCHLT named entity annotated resources, a collection of named entity annotated data and Conditional Random Field-based named entity recognisers for ten of the official languages. In this work, we provide a detailed description and linguistic analysis of the named entity (NE) annotated data for the agglutinative isiXhosa language, by analysing the morphosyntactic features relevant to the three main types of NE, viz. person, location, and organisation. From the data, we identify suffix and capitalisation features that may be good predictors of the different NE types. Based on these features, we describe the named entity recogniser and feature set developed as part of the NCHLT release. The recogniser has high precision, 0.9713 overall, but relatively low recall, 0.7409, especially for person names, 0.5963, resulting in an overall F-score of 0.8406. Although there are various avenues to improve the named entity recogniser, this is a significant release for a historically under-resourced language.