To prevent further brain tumour growth, malignant tissue should be removed as completely as possible in neurosurgical operations. Therefore, differentiation between tumour and brain tissue as well as detecting functional areas is very important. Hyperspectral imaging (HSI) can be used to get spatial information about brain tissue types and characteristics in a quasi-continuous reflection spectrum. In this paper, workflow and some aspects of an adapted hardware system for intraoperative hyperspectral data acquisition in neurosurgery are discussed. By comparing an intraoperative with a laboratory setup, the influences of the surgical microscope are made visible through the differences in illumination and a pixel- and wavelength-specific signal-to-noise ratio (SNR) calculation. Due to the significant differences in shape and wavelength-dependent intensity of light sources, it can be shown which kind of illumination is most suitable for the setups. Spectra between 550 and 1,000 nm are characterized of at least 40 dB SNR in laboratory and 25 dB in intraoperative setup in an area of the image relevant for evaluation. A first validation of the intraoperative hyperspectral imaging hardware setup shows that all system parts and intraoperatively recorded data can be evaluated. Exemplarily, a classification map was generated that allows visualization of measured properties of raw data. The results reveal that it is possible and beneficial to use HSI for wavelength-related intraoperative data acquisition in neurosurgery. There are still technical facts to optimize for raw data detection prior to adapting image processing algorithms to specify tissue quality and function.:Abstract Introduction Materials and methods (Clinical workflow and setup for hyperspectral imaging process, Characteristics of the lighting, Characteristics of the hyperspectral imaging camera, Spectral data acquisition and raw data pre-processing in neurosurgery, Spectral data evaluation) Results (Spectral characteristics of the lighting, SNR of the HSI camera, Data acquisition and raw data preprocessing during neurosurgical operation, Spectral data evaluation) Discussion Conclusions