The ability to quantify and qualify subtle differences between milk powders is very advantageous to industrial manufacturers. Hyperspectral imaging (HSI) combines the spatial attributes of image processing with the chemical diagnostic attributes of spectroscopy, and was evaluated to determine if it could be used to discriminate between milk powders produced in various factories, and of differing functional qualities, such as dispersibility. The results showed that HSI can achieve these aims when multivariate analysis techniques such as Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression are applied. The PCA results showed that the most obvious differences were in the first and second principal components. Strategies to pre-process hyperspectral data, and to optimally automatically detect and remove artefacts in the images were also established. The PLS results showed that the information from HSI can be used to predict with reasonable accuracy the key functional property of dispersibility, and is the first step in a ‘real-time quality’ initiative to establish correlations between hyperspectral images and key quality attributes of milk powder either on, or at-line in close to real-time.