This paper presents an exploratory investigation of the use of image analysis and hardness analysis of barley kernels for characterisation and prediction of malting quality. A sample set of fifty barley samples representing 15 spring barley and 10 winter barley varieties grown at two locations in Denmark was used. The samples were micro-malted and mashed and analysed for 13 quality parameters according to the official methods of the European Brewery Convention. A sub-sample of the barley samples was analysed on two different single kernel instruments: (1) Foss Tecator GrainCheck was applied for non-destructive recording of single kernel size and shape (width, length, roundness, area, volume and total light reflectance) and (2) Perten Single Kernel Characterization System 4100 was applied for single kernel hardness and weight determinations. The eight variables from these single seed analyses have been used in two different ways, either as means and standard deviations, or as appended histogram spectra representing 250 kernels from each bulk sample. By the two methods, it has been possible to obtain reasonable Partial Least Squares Regression (PLSR) models for the structural and physical part of the malting quality complex associated to malt modification, but it was as expected impossible to predict the biochemical parameters associated with nitrogen chemistry and enzymatic power. The best model was achieved for (1→3, 1→4)-β-D-glucan in barley. The hardness of the barley kernels is by far the most important variable for describing malting performance. The additional use of the morphological data as acquired by fast non-destructive image analysis, however, also reveals some malting quality information by improving the calibration models based on hardness alone. The brightness of the kernels is by far the most important GrainCheck variable but also kernel size and shape is associated to malting performance. In general, the utilisation of the single kernel readings (used as histogram spectra), compared to sample mean and standard deviation, did not provide additional information for an improved prediction of the malting quality parameters.