Abstract: This invention identifies anomalies in a data stream, without prior training, by measuring the difficulty in finding similarities between neighbourhoods in the ordered sequence of elements. Data elements in an area that is similar to much of the rest of the scene score low mismatches. On the other hand a region that possesses many dissimilarities with other parts of the ordered sequence will attract a high score of mismatches. The invention makes use of a trial and error process to find dissimilarities between parts of the data stream and does not require prior knowledge of the nature of the anomalies that may be present. The method avoids the use of processing dependencies between data elements and is capable of a straightforward parallel implementation for each data element. The invention is of application in searching for anomalous patterns in data streams, which include audio signals, health screening and geographical data. A method of error correction is also described.