Content processing is a vast and growing field that integratesdifferent approaches borrowed from the signal processing,information retrieval and machine learning disciplines. Inthis article we deal with a particular type of content pro-cessing: the so-called content-based transformations. We willnot focus on any particular application but rather try to givean overview of different techniques and conceptual implica-tions. We first describe the transformation process itself,including the main model schemes that are commonly used,which lead to the establishment of the formal basis for a definition of content-based transformations. Then we take aquick look at a general spectral based analysis/synthesisapproach to process audio signals and how to extract featuresthat can be used in the content-based transformation context.Using this analysis/synthesis approach we give some exam-ples on how content-based transformations can be applied tomodify the basic perceptual axis of a sound and how we caneven combine different basic effects in order to perform moremeaningful transformations. We finish by going a step furtherin the abstraction ladder and present transformations that arerelated to musical (and thus symbolic) properties rather thanto those of the sound or the signal itself.