The tone reproduction curve (TRC) is a representation of a printer's input-output mapping for each primaty color. It is a two dimensional signal with both temporal and tonal characteristics. With an appropriate signal model that represents the TRC, the entire time- varying TRC can be reconstructed from measurements of a few time-sequential scheduled print patches. The reconstructed TRC can then be used as feedback signal for control systems to compensate for any TRC variations. In the past, signal models based on Fourier basis and principal components analysis have been proposed for the design and analysis of the sampling sequence and the reconstruction filter. However, the tone reproduction has localized features in that it variation is less at some tones than at others. These features have not been exploited in previous signal models but can potentially improve the effectiveness of sampling and reconstruction algorithms. In this paper, the localized nature of wavelets is used to capture the ápriori knowledge of these local features and a wavelet based representation of the time-varying TRC is proposed. The wavelet based model is obtained from a track of experimentally obtained time-varying TRC data. The usefulness of this approach is demonstrated from the reconstruction of a time-sequentially sampled TRC.