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Unsupervised machine learning provides an under-utilised set of tools for increasing the objectivity associated with scientific investigation and discovery. We present two new solar wind origin classification schemes developed using a variety of the techniques available. The schemes aim to classify solar wind into three types: coronal hole wind, streamer belt wind, and ‘unclassified' which does not fit into either of the previous two categories. The classification schemes are created using non-evolving solar wind parameters, such as ion charge states and composition, measured during Ulysses' three fast-pass latitude-scans. The schemes are subsequently applied to the whole of the Ulysses and ACE datasets. Given the choice of parameter type, the scheme is grounded in the physical properties of the solar source regions. Furthermore, the techniques used are selected specifically to reduce the introduction of subjective biases. We demonstrate significant ‘best case’ disparities (7% - 18%) with the traditional "fast" and "slow" solar wind determined using speed thresholds.