The collection of IR spectra through microscope optics and the visualization of the IR data by IR imaging represent a visualization approach, which uses infrared spectral features as a native intrinsic contrast mechanism. To illustrate the potential of this spectroscopic methodology in breast cancer research, we have acquired IR-microspectroscopic data from benign and malignant lesions in breast tissue sections by point microscopy with spot sizes of 30-40 microm. Four classes of distinct breast tissue spectra were defined and stored in the data base: fibroadenoma (a total of 1175 spectra from 14 patients), ductal carcinoma in situ (a total of 1349 spectra from 8 patients), connective tissue (a total of 464 spectra), and adipose tissue (a total of 146 spectra). Artifical neural network analysis, a supervised pattern recognition method, was used to develop an automated classifier to separate the four classes. After training the artifical neural network classifier, infrared spectra of independent external validation data sets ("unknown spectra") were analyzed. In this way, all spectra (a total of 386) taken from micro areas inside the epithelium of fibroadenomas from 4 patients were correctly classified. Out of the 421 spectra taken from micro areas of the in situ component of invasive ductal carcinomas of 3 patients, 93% were correctly identified. Based on these results, the potential of the IR-microspectroscopic approach for diagnosing breast tissue lesions is discussed.