The occurrence of different forms of asymmetry complicates the analysis and interpretation of patterns in asymmetry. Furthermore, between-individual heterogeneity in developmental stability (DS) and thus fluctuating asymmetry (FA), is required to find relationships between DS and other factors. Separating directional asymmetry (DA) and antisymmetry (AS) from real FA and understanding between-individual heterogeneity in FA is therefore crucial in the analysis and interpretation of patterns in asymmetry. In this paper we introduce and explore mixture analysis to (i) identify FA, DA and AS from the distribution of the signed asymmetry, and (ii) to model and quantify between-individual heterogeneity in developmental stability and FA. In addition, we expand mixtures to the estimation of the proportion of variation in the unsigned FA that can be attributed to between-individual heterogeneity in the presumed underlying developmental stability (the so-called hypothetical repeatability). Finally, we construct weighted normal probability plots to investigate the assumption of underlying normality of the different components. We specifically show that (i) model selection based on the likelihood ratio test has the potential to yield models that incorporate nearly all heterogeneity in FA; (ii) mixtures appear to be a powerful and sensitive statistical technique to identify the different forms of asymmetry; (iii) restricted measurement accuracy and the occurrence of many zero observations results in an overestimation of the hypothetical repeatability on the basis of the model parameters; and (iv) as judged from the high correlation coefficients of the normal probability plots, the underlying normality assumption appears to hold for the empirical data we analysed. In conclusion, mixtures provide a useful statistical tool to study patterns in asymmetry.