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In this paper, a fully connected recurrent neural network (RNN) is presented for the recovery of M-ary pulse amplitude modulated (M-PAM) signals in the presence of intersymbol interference and additive white Gaussian noise. The network makes use of two different activation functions. One is the traditional two-level sigmoid function, which is used at its hidden nodes, and the other is the M-level sigmoid function (MSF), which is used at the output node. The shape of the M-level activation function is controlled by two parameters: the slope and shifting parameters. The effect of these parameters on the learning performance is investigated through extensive simulations. In addition, the network is compared with a linear transversal equalizer, a decision feedback equalizer and a recently proposed RNN equalizer which has used a scaled sigmoid function (SSF) at its output node. Comparisons are made in terms of their learning properties and symbol error rates. It is demonstrated that the proposed RNN equalizer performs better, provided that the MSF parameters are properly selected