Carbon fibre reinforced polymer (CFRP) composites are well-known for their specific strength and stiffness properties. For the past two decades, various industries have increasingly used these materials to improve performance and reduce emissions. This increased use has caused a problematic growth in both production and end-of-life CFRP waste. Recent developments in recycling processes allow recycled carbon fibres (rCF) to have undamaged and clean surfaces with intact sizing, retaining the mechanical properties of non-recycled virgin carbon fibres (vCF) at a considerable cost reduction. Unfortunately, CF recyclate from these recycling procedures is highly diffuse making them useless for structural application. For making rCF material viable to be used in structural applications, further processing of the recyclate is necessary. To manufacture high-performance rCF materials which feature homogenously distributed, orientated and aligned fibres, considerable effort has to be spent in controlling the microstructure during processing. The presented thesis has focused on introducing an initial characterisation method that allows for quantitative analysis and comparison between rCF microstructures. Characterisation is performed by analysing elliptical CF footprints in perpendicular cross-section micrographs. Fibres in the micrograph are identified and segmented through a semi-supervised machine learning tool. The elliptical fibre shape is measured by the method of ellipses (MoE) in FIJI ImageJ. This MoE extracts the position and elliptical shape of every fibre footprint in the micrograph. Analysis of the microstructure is done in python to quantify the microstructural morphology by fibre diameter, fibre orientation distribution (FOD), global-local fibre volumes (Vf), fibre density distribution (FDD) and Fibre-to-Fibre (FtF) spacing. It was found that the method is accurate and reliable in identifying and measuring fibres present in the cross-section micrograph. Similarly, calculated values for Vf, FDD and FtF spacing can be used for characterising the quality of studied materials. Unfortunately, despite being frequently used in literature to characterise highly aligned and orientated CF materials, all fibre orientation measurements were flawed by the inherent susceptibility of the MoE to calculate fibre orientation with large biases. This orientation bias was caused by the error sensitivity of the arccosines function used for calculating the out-of-plane (OoP) angle. Minute elliptical shape differences in near perpendicular fibres with almost circular footprints resulted in large non-linear errors. Recommended is to study FODs in samples that are sectioned at a 45 or 60° angle, resulting in large elliptical fibre footprints which are less error sensitive when analysing with the MoE. The induced section angle can easily be transformed back to yield an unbiased FOD measurement.