A new modeling technique, based on independent component analysis (ICA), is proposed to represent and recognize high-dimensional samples from a large set of classes. The model is constructed via density estimation techniques, and recognition is performed in the Bayesian decision framework. We show that the technique can be successfully used for automatic object identification in environments where a visual observer is faced with a classification problem in high-dimensional spaces with a large number of classes. 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 of images corresponding to 400 different objects. It is also shown how our approach outperforms other techniques commonly used in the context of appearance-based recognition. © 2004 Taylor and Francis.