Nailfold capillaroscopy (NC) is a non-invasive imaging technique employed to assess the condition of blood capillaries in the nailfold. It is particularly useful for early detection of scleroderma spectrum disorders and evaluation of Raynaud's phenomenon. While diagnosis based on NC is typically performed by manual inspection, computerised nailfold capillaroscopy can help to reduce the inherent ambiguity present in human judgement while greatly reducing the time for diagnosis. Diagnosis of NC images involves the recognition of early, active and late patterns, also known as NC patterns or scleroderma (SD) patterns, in the images. In this paper, we propose a holistic method to classify NC images in these well known patterns. In particular, we employ texture analysis to describe the underlying patterns, coupled with a classifier to first identify patterns in fingers, and then, through a voting strategy, reach a decision for a patient. Experimental results on a set of NC images with known ground truth demonstrate the efficacy of our approach.