Summary: Intrinsic classification, or unsupervised learning of a classification, was the earliest application of what is now termed an Minimum Message Length (MML) or Minimum Description Length (MDL) inference. The MML algorithm `Snob' and its relatives have been used successfully in many domains. These algorithms treat the `things' to be classified as independent random selections from an unknown population whose class structure, if any, is to be estimated. This work extends MML classification to domains where the `things' have a known spatial arrangement and it may be expected that the classes of neighbouring things are correlated. Two cases are considered. In the first, the things are arranged in a sequence and the correlation between the classes of successive things modelled by a first-order Markov process. An algorithm for this case is constructed by combining the Snob algorithm with a simple dynamic programming algorithm. The method has been applied to the classification of protein secondary structure. In the second case, the things are arranged on a two-dimensional (2D) square grid, like the pixels of an image. Correlation is modelled by a prior over patterns of class assignments whose log probability depends on the number of adjacent mismatched pixel pairs. The algorithm uses Gibbs sampling from the pattern posterior and a thermodynamic relation to calculate message length.