The present work is a demonstration of how near infrared (NIR) hyperspectral photoluminescence imaging can be used to detect defects in silicon wafers and solar cells. Chemometric analysis techniques such as multivariate curve resolution (MCR) and partial least squares discriminant analysis (PLS-DA) allow various types of defects to be classified and cascades of radiative defects in the samples to be extracted. It is also demonstrated how utilising a macro lens yields a spatial resolution of 30 µm on selected regions of the samples, revealing that some types of defect signals originate in grain boundaries of the silicon crystal, whereas other signals show up as singular spots. Combined with independent investigation techniques, hyperspectral imaging is a promising tool for determining origins of defects in silicon samples for photovoltaic applications.