Abstract Near-infrared Raman spectroscopy (NIRS) is one of the novel techniques that has a potential for in vivo diagnosis of atherosclerosis in human arteries. For such real time clinical applications, a rapid collection and analysis of the data is needed. One of the major problems with the fast data collection is that the noise generated by the detector has the same level as the Raman signal from the tissue, which makes the analysis difficult. In this work, NIRS measurements have been carried out on a total of 60 samples from human coronary arteries. Raman spectral data with the correlated histopathological analysis have been used as a basis to stimulate the cases of severe noise conditions. The main objective of this paper is the comparison of different processing algorithms that have been developed based on either wavelet transformation or principal component analysis for compressing the Raman spectral vectors and a rapid data classification based on different neural network architectures. The developed algorithms found to provide promising diagnosis results with classification errors smaller than 5%, even in the cases of Raman data with collection times as small as 20 ms. It has been concluded that the developed algorithms would be very much useful in the development of Raman spectroscopy systems for in vivo biological applications.