The measurement of a distributed property is frequently encountered in engineering applications. The impact of a possible measurement error on the measured distribution is described and two methods to account for such an error are detailed: A Fourier transform based deconvolution procedure and a moment based deconvolution approach via kernel estimators are presented. The deconvolution methods can be used in many particle characterization and process applications where particle features or properties are commonly presented in a distributed form. In the current instance, these methods are carefully explored by using artificially generated data and applied to experimental data obtained from filter cake thickness measurements.