The issue addressed in this paper is the unsupervised learning of observed shapes. More precisely, we are aiming at learning the main features of an object seen in different scenarios. We adapt the statistical framework from [1] to propose a model in which an object is described by independent classes representing its variability. Our work consists in proposing an algorithm which learns each class characteristics in a sequential way: each new observation will improve our object knowledge. This algorithm is particularly well suited to real time applications such as shape recognition or classification, but turns out to be a challenging problem. Indeed, the so-called classic machine learning algorithms in missing data problems such as the Expectation Maximization algorithm (EM) are not designed to learn from sequentially acquired observations. Moreover, the so-called hidden data simulation in a mixture model can not be achieved in a proper way using the classic Markov Chain Monte Carlo (MCMC) algorithms, such as the Gibbs sampler. Our proposal, among other, takes advantage from the contribution of Cappé and Moulines [2] for a sequential adaptation of the EM algorithm and from the work of Carlin and Chib [3] for the hidden data posterior distribution simulation.