Method of identification of objects in images characterised in that it comprises the following stages: (i) a feature extraction stage including the following steps for both: reference images, i.e. images representing each at least a single reference object, and at least one query image, i.e. an image representing unknown objects to be identified: (a) identification of key-points, i.e. salient image regions: (b) post-processing of key-points; (c) computation of the descriptors, i.e. representations, of the key-points, (ii) an indexing stage of reference images including the following steps: (a) key-point extraction; (b) post-processing of key-points; (c) assignment of key-points to visual words of a visual word vocabulary created from a collection of training images, wherein the visual words are centres of clusters of key-point descriptors; (d) addition of key-points to an inverted file structure, wherein the inverted file structure comprises a hit list for every visual word that stores all occurrences of the word in the reference images and wherein every hit stores an identifier of the reference image where the key-point was detected; and (iii) a stage of recognition of objects present in the query image including the following steps: (a) key-point extraction; (b) post-processing of key-points; (c) assignment of key-points to visual words of the visual word vocabulary; (d) for each pairing of a key-point from the query image and one of the hits assigned to the same visual word aggregating a vote into an accumulator corresponding to the reference image of the hit; and (e) identification of the matching scores corresponding to the reference images based on the votes of the accumulators. characterised in that the post-processing comprises: - normalizing key-point scales according to the size of reference objects; and - eliminating key-points with normalized scales smaller than a predefined value. (i) a feature extraction stage including the following steps for both: reference images, i.e. images representing each at least a single reference object, and at least one query image, i.e. an image representing unknown objects to be identified: (a) identification of key-points, i.e. salient image regions: (b) post-processing of key-points; (c) computation of the descriptors, i.e. representations, of the key-points, (a) identification of key-points, i.e. salient image regions: (b) post-processing of key-points; (c) computation of the descriptors, i.e. representations, of the key-points, (ii) an indexing stage of reference images including the following steps: (a) key-point extraction; (b) post-processing of key-points; (c) assignment of key-points to visual words of a visual word vocabulary created from a collection of training images, wherein the visual words are centres of clusters of key-point descriptors; (d) addition of key-points to an inverted file structure, wherein the inverted file structure comprises a hit list for every visual word that stores all occurrences of the word in the reference images and wherein every hit stores an identifier of the reference image where the key-point was detected; and (a) key-point extraction; (b) post-processing of key-points; (c) assignment of key-points to visual words of a visual word vocabulary created from a collection of training images, wherein the visual words are centres of clusters of key-point descriptors; (d) addition of key-points to an inverted file structure, wherein the inverted file structure comprises a hit list for every visual word that stores all occurrences of the word in the reference images and wherein every hit stores an identifier of the reference image where the key-point was detected; and (iii) a stage of recognition of objects present in the query image including the following steps: (a) key-point extraction; (b) post-processing of key-points; (c) assignment of key-points to visual words of the visual word vocabulary; (d) for each pairing of a key-point from the query image and one of the hits assigned to the same visual word aggregating a vote into an accumulator corresponding to the reference image of the hit; and (e) identification of the matching scores corresponding to the reference images based on the votes of the accumulators. characterised in that the post-processing comprises: - normalizing key-point scales according to the size of reference objects; and - eliminating key-points with normalized scales smaller than a predefined value. (a) key-point extraction; (b) post-processing of key-points; (c) assignment of key-points to visual words of the visual word vocabulary; (d) for each pairing of a key-point from the query image and one of the hits assigned to the same visual word aggregating a vote into an accumulator corresponding to the reference image of the hit; and (e) identification of the matching scores corresponding to the reference images based on the votes of the accumulators. - normalizing key-point scales according to the size of reference objects; and - eliminating key-points with normalized scales smaller than a predefined value.