This paper applies a bayesian classification scheme to the problem of recognition through probabilistic modeling of high dimensional data. In this context, high dimensionality does not allow precision in the density estimation. We propose a local Independent Component Analysis (ICA) representation of the data. Here, the components can be assumed statistically independent and, in many cases, sparsity is observed. We show how these two characteristics can be used to simplify and add accuracy to the density estimation and develop bayesian decision within this representation. A first experiment illustrates that classification using an ICA representation is a technique that, even in low dimensions, performs comparably to standard classification techniques. The second experiment tests the ICA classification model on high dimensional data. Recognition was performed using local color histograms as salient features. It is also shown how our approach outperforms other techniques commonly used in the context of appearance-based recognition.