The list of "d"-"I" data pairs is highly characteristic of a crystal phase and, thus, suitable for the identification, also called ‘fingerprinting’, of crystal phases. Search-match algorithms compare selected test reflections of an unknown crystal phase with entries in the database. Intensity-driven algorithms utilize the three most intense lines (so-called ‘Hanawalt search’), while "d"-spacing-driven algorithms are based on the eight to ten largest "d"-spacings (so-called ‘Fink search’). X-ray powder diffraction fingerprinting has become the standard tool for the identification of single or multiple crystal phases and is widely used in such fields as metallurgy, mineralogy, forensic science, archeology, condensed matter physics, and the biological and pharmaceutical sciences. Powder diffraction patterns of very small single crystals, or crystallites, are subject to size-dependent peak broadening, which, below a certain size, renders powder diffraction fingerprinting useless. In this case, peak resolution is only possible in 3D reciprocal space, i.e. by applying single-crystal electron diffraction techniques. High-Resolution Transmission Electron Microscopy (HRTEM) provides images and diffraction patterns of nanometer sized crystallites. Fourier transforms of HRTEM images and electron diffraction patterns both supply information about the projected reciprocal lattice geometry for a certain crystal orientation, where the projection axis coincides with the optical axis of the microscope. Projected lattice geometries can be represented by so-called ‘lattice-fringe fingerprint plots’ (LFFPs), also called angular covariance plots. The horizontal axis of such a plot is given in reciprocal lattice length and is limited by the point resolution of the microscope. The vertical axis is defined as acute angle between Fourier transformed lattice fringes or electron diffraction spots. A 2D data point is defined by the length of a reciprocal lattice vector and its (acute) angle with another reciprocal lattice vector. Sets of 2D data points that obey Weiss’s zone law are subsets of the entirety of data points in an LFFP. A suitable search-match algorithm using LFFPs, therefore, tries to find matching zone axis subsets in the database. It is, essentially, a variant of a lattice matching algorithm. In the case of electron diffraction patterns, structure factor amplitudes can be used, in a later step, to further discern among a selection of candidate structures (so-called 'structure factor fingerprinting').