Deconvolution allows the reconstruction of non-accessible inputs (e.g. hormone secretion rate) from their causally-related measurable effects (e.g. hormone plasma concentration). Deconvolution is challenging under several aspects both general (e.g. determination of a suitable trade-off between data fit and solution smoothness in order to contrast ill-conditioning, assessment of the confidence intervals) as well as specific of physiological systems (e.g. non-uniform and infrequent data sampling). Recently, a stochastic regularization approach has been proposed and validated to handle these difficulties (De Nicolao et al., Automatica 33 (1997) 851-870). In this paper, an interactive program, WINSTODEC, is presented to allow the clinical investigator to easily obtain the solution of a deconvolution problem by this approach.