This paper presents a formal methodology for the analysis of process signals and the automatic extraction of temporal features contained in a record of measured data. It is based on the multiscale analysis of the measured signals using wavelets, which allows the extraction of significant temporal features that are localized in the frequency domain, from segments of the record of measured data (i.e. localized in the time domain). The paper provides a concise framework for the multiscale extraction and description of temporal process trends. The resulting algorithms are analytically sound, computationally very efficient and can be easily integrated with a large variety of methods for the interpretation of process trends and the automatic learning of relationships between causes and symptoms in a dynamic environment. A series of examples illustrate the characteristics of the approach and outline its use in various settings for the solution of industrial problems.