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The number of neighbour nodes within the direct communication range for a particular node can vary significantly due to the dynamic nature of the wireless communication networks. For a large number of neighbours, it is feasible to obtain an estimation of the number of neighbours very quickly. Moreover, identification (collecting the identities) of the neighbours gives more accurate information about them. Sometimes, signal propagation between nodes has an impact on theseprocedures. For terrestrial communication networks the signals suffer only spreading loss for electromagnetic (EM) waves. In underwater communication networks, signals suffer spreading and absorption loss for both EM and acoustic waves. Due to the difference in signal propagation, the capture effect (the reception of a packet even from the collision) also differs in different networks. Furthermore, there are long propagation delays in underwater acoustic networks (UANs) due to the low speed of propagating signals, and in long-range EM communications, such as space communication networks (SCNs). Current approaches to estimation and identification fail to cope with either long-delay or the capture effect. In this work, we present a procedure for estimation of number of neighbours which takes into account the capture effect and propagation delay into consideration so that it can be used in any type of network. A concurrent procedure for estimating the neighbour’s spatial dimensionality, which determines whether the neighbours are oriented in 1D, 2D or 3D, is also given. The time taken for estimation is independent of the number of neighbours, and is suitable for networks of all sizes. By a performance and sensitivity analysis, we show the robustness of the dimensionality estimation procedure.Finally, we propose a procedure for identifying the neighbours for any longdelay networks (LDNs), such as UANs and SCNs using either un-slotted or slotted protocols, which are insensitive to the propagation delay. I