Computer vision tasks such as recognition and classification of objects and structures or image registration and retrieval can provide significant information when applied to microscopy images. Recently developed techniques for the detection and description of local features make the extraction and description of local image features that are invariant to various changes possible. The invariance and robustness of feature detection and description techniques play a key role in the design and implementation of object recognition, image registration, or image mosaicing applications. The scale-invariant feature transform (SIFT) technique is a widely used method for the detection, description, and matching of image features. In this article we present the results of our experiments regarding the repeatability of SIFT features, and to the precision of the SIFT feature matching, under image modifications specific to confocal scanning laser microscopy (CSLM). We have analyzed the behavior of SIFT while changing the pinhole aperture, photomultiplier gain, laser beam power, and electronic zoom. Our experiments, conducted on CSLM images, show that the SIFT technique is able to match detected key points between images acquired at different values of the acquisition parameters with good precision and represents a consistent tool for computer vision applications in CSLM.