Automatic classification of analog and digital modulation signals plays an important role in communication application such as an intelligent demodulator, interference identification and monitoring. The automatic recognition of the modulation format of a detected signal, the intermediate step between signal detection and demodulation, is a major task of an intelligent receiver, with various civilian and military applications. This paper presents a new approach for automatic modulation classification for digitally modulated signals. This method utilizes a signal representation known as the modulation model. The modulation model provides a signal representation that is convenient for subsequent analysis, such as estimating modulation parameters. The modulation parameters to be estimated are the carrier frequency, modulation type, and bit rate. The modulation model is formed via autoregressive spectrum modeling. The modulation model uses the instantaneous frequency and bandwidth parameters as obtained from the roots of the autoregressive polynomial. This method is also classifies accurately under low carrier to noise ratio (CNR). This paper is also presents an improved version of S-Transform for time frequency analysis of different digitally modulated signals to observe variations of amplitude, frequency and phase.