Multivariate techniques and especially cluster analysis have been commonly used in Archaeometry. Exploratory and model-based techniques of clustering have been applied in geochemical (continuous) data of archaeological artifacts for provenance studies. Model-based clustering techniques like classification maximum-likelihood and mixture maximum likelihood had been used in a lesser extent in this context and although they seem to be suitable for such data, they either present practical difficulties-like high dimensionality of the data-or their performance give no evidence to support that they prevail on the standard methods (Papageorgiou et al., 2001). In this paper standard statistical methods (hierarchical clustering, principal components analysis) and the recently developed one of the multivariate mixture of normals with unknown number of components (see Dellaportas and Papageorgiou, 2005) in the category of the model-based ones, are applied and compared. The data set comprises of chemical compositions in 188 ceramic samples derived from the Aegean islands and surrounding areas.